Thursday, May 31, 2018

A Guide to Some of My Blog Posts, Hither and Yon






I've been blogging for several years now.  I first started blogging at the Graaskamp Center for Real Estate, when we had control of our own website.  But "central planning" won out at the Wisconsin School of Business, as the School determined that in today's world, the benefits of a school-wide format and control over content exceeded the benefits of bottom-up content. Probably the right call, though I was sorry I no longer had the opportunity to bloviate at that location.

Once I "retired" in 2016 -- notice the quotes! -- I started my own blog, at this location. Some months later, my friend Morris Davis, Academic Director of Rutgers Center for Real Estate, started a very ambitious blog, and recruited yours truly and noted macroeconomist Julia Coronado to provide the majority of blog entries.

So my blog entries are scattered around at three different locations.  This post is a directory of  a number of my favorite posts, organized thematically , rather than chronologically or by the location of the post.

This post will be updated from time to time as I write more posts or find the odd one in some forgotten corner of this inter-web-net thing.

By the way, there are a lot of great blog posts at Rutgers written by Julia, Morris, and other colleagues.  Check them out!

And see the long list of "Malpezzi Favorites" to the right of this column for links to other interesting blogs and sources.

Back to my blogging.  I've got a wide range of interests, but I've written more about housing than any other single subject, so let's start there.


NB: * means it's one of my favorites.


Housing Posts


The first batch are mostly domestic, although over time I'll post more about my international work.

Housing supply and demand: some basics

*A first look at housing “affordability” in New Jersey and the United States

*Low-income housing programs that work: 3 posts on vouchers

*Low-income housing programs that work: 3 posts on land use and development regulation, NIMBYs and YIMBYs:

Introduction to the city

The complex role of immigration in our cities

The Wisconsin Idea meets Barcelona:
Part I
Part II
Part III

Crime: how bad is it?


Other Real Estate



*"I believe you have my stapler."  A journey through office space from Babylonian scribes to Milton Waddams to Apple’s ‘mothership’


Economic Indicators, The Aggregate Economy





The Wisconsin Idea


Wisconsin Real Estate: A Century of Tradition, and Innovation

*Values of the Wisconsin Real Estate Program

A Little Graaskamp Center History

Michael Brennan


Reading for Life


The original "Reading for Life" list -- as amended, May 2012 -- can be found here.

Later blog posts on the topic:
Observations on essays, blogging, and reading for life
Ben Bernanke's "Reading for Life?"
Bobos in Paradise

Nature's Metropolis
Reading for Life on the financial crisis
Summer reading (The Winner's Curse, This Time is Different)


Me, me, me.  It's all about me.


I retired.

I haven't quit.

The IMF's Hites Ahir and Prakash Loungani were kind enough to interview me.  (And lots of other interesting people, check it out.

If you Google "Malpezzi," you get one of my brothers first.



Sad News


Remembrance for Arthur Goldberger

In memoriam, John Quigley

Chip Case, top housing economist and master teacher, passes away


Miscellany


Economics and elections

"The Calculator:" A peaen to the HP 12-C

A digression on brutalist architecture








Thursday, January 25, 2018

A First Look at the Tax Cut and Jobs Act, the Economy, and Real Estate


UPDATE May 2018


In May 2018 David Barker and Steve Malpezzi presented abridged and revised versions of our preliminary analysis of the Tax Cut and Jobs Act to the professional side of the Homer Hoyt Fellows.

Malpezzi presented contextual data on government taxation, spending, deficits, debt, etc., and on possible macroeconomic effects of the TC&JA.  Download Malpezzi's presentation here.

Barker presented detailed discussion of the TC&JA's potential effects on real estate markets, including several case studies.  Download Barker's presentation here.

Those presentations were developed from a larger effort that involved several other colleagues that began in January.  That effort, and the larger library of draft presentation slides, are presented below.

The slides above are cleaner and more concise; however they are still preliminary and not meant as tax or investment advice.

The slides below are less polished (!) and more extensive; they may be of interest to instructors who are searching for selected slides for class use.  Or for those who are gluttons for PowerPoint punishment.


Original January post and files follow here:



In January 2018, we held a meeting of the Homer Hoyt Academic Fellows in West Palm Beach, Florida.  Much more about the Hoyt Group another day, soon.  For now suffice it to say that the meeting gathers about 50 real estate researchers, mainly but not entirely economists, for several days of papers and presentations.

One of the sessions was devoted to a semi-structured group discussion of the Tax Cut and Jobs Act 2017, including but not limited to provisions affecting real estate.

To help frame the discussion, we prepared some draft materials, and pointed colleagues to a few other sources. This blog post provides links to files containing that information, so that meeting participants – and now, other readers of this blog – can readily access these materials.

Taxes are complicated things, as I re-discovered recently when my tax software choked on my own return for the year I split between Wisconsin and Massachusetts. I finished my returns “by hand” and submit them by paper mail. Wish me luck!

Which reminds me that it's a good place to place a disclaimer.  These notes are working drafts, written by economists who are not tax advisors.  As you will see, there are several areas where the TC&JA is not very clear, and these materials should not be used as substitutes for professional advice.

The draft materials will be revised.  Comments and corrections are extremely welcome, especially at this early stage.  The current version is dated April 30, 2018.

For the moment this post points you to two links.

The first link downloads a PDF summary of tax provisions, comparing the gist of major provisions before and after the Tax Cut and Jobs Act. Again we reiterate that these are brief summaries and not a substitute for professional tax advice.

The second link downloads a PowerPoint presentation that includes slides we used in the discussion, including the summary table of major provisions noted above, but also a number of other slides, about 100 in total. Quite a few of the slides are culled from my class notes on public finance, others were contributed by Homer Hoyt participants including David BarkerRichard Green and Morris Davis. Since I pulled together the final deck, with a lot of my own slides, David Richard and Morris should not be held responsible for any of my outrageous personal opinions remaining.

They are grouped within the deck roughly as follows:


  1. Taxes – some basics.
  2. Spending – some basics.
  3. Deficits and debt – some basics.
  4. What about state and local taxes?
  5. Review of the Tax Cut and Jobs Act 2017; comparison to prior code.
  6. TC&JA and real estate – focus on pass-through entities.
  7. TC&JA: who gets the cuts?
  8. TC&JA:  will it increase investment?
  9. TC&JA:  will it increase GDP, wages and incomes?
  10. Some readings.


Be aware that most of the slides also have notes attached, some of them fairly extensive. A preliminary bibliography is included as a long note to a slide near the end. Look for the "Tax Library in Hell" slide.  The bibliography is on the next slide.

Monday, January 8, 2018

Housing Supply and Demand: Some Basics



Teach a parrot the terms "supply and demand" and you've got an economist.  (Thomas Carlyle)


(Interesting digression: Carlyle is also usually cited as the first to apply the term “the dismal science” to economists.  This was in the context of a debate between the pro-slavery Carlyle and anti-slavery economists (notably John Stuart Mill) regarding the reintroduction of slavery to the West Indies.  See Persky (1990)).


Functions Give Rise to Curves


Supply and demand, the fundamental behavior of producers and consumers, are each multivariate concepts.  Demand for housing depends on income, housing prices, demographics (population, household formation, the age distribution), mortgage rates and availability, and certain taxes, among other fundamentals.  Some of these are relatively easy to measure, or at least they are conceptually straightforward; but there are other demand fundamentals that are a little “squishy,” at least to most economists – psychology, tastes, and expectations come to mind.  

Both the levels of variables and rates of change in those variables can be considered.  Furthermore, demand is forward-looking and will depend not just on today’s values of the variables, but on expectations about their future values.

On the producer side, supply is affected by housing prices, the prices of inputs, the technology of building and development, infrastructure availability, physical geography and topography, interest rates (especially short term, to builders), other taxes, and the regulatory environment, among other things.  Expectations and psychology, levels and changes, and psychology and so-called “animal spirits” matter hear, as well.

The familiar supply and demand curves pick a single variable (most often the price of housing) to analyze, while at least temporarily holding other things (income, prices of inputs, etc.) constant.  Exhibit 1 illustrates, presenting market-level supply and demand curves.



Even though the curves usually look similar when drawn, conceptually it’s important to distinguish between the demand function (and curves) for individual consumers, and the sum of these over all consumers in a market.  In this section we focus on market supply and demand.  Later we’ll examine supply and demand for individual producers and consumers, as well as for market aggregates.

Suppose, for simplicity, that all housing units are the same, so that we can measure quantity by simply counting houses; then rent per house is the same as the flow price per unit of housing services, and the value or asset price per house would also be a true price measure.  Holding for the moment the other variables that affect supply and demand fixed, we highlight the effect of prices on both supply and demand.  Demand slopes down – the higher the price, the less we demand.  Supply, using similar reasoning, slopes upwards.  If supply was fixed, the supply curve would be vertical.  If supply was horizontal, that would indicate that the market would supply any quantity demanded, at a constant market price.  As drawn, the supply curve is fairly flat – meant to convey fairly, but not perfectly, elastic supply.

Now let’s change one of the other variables, which we initially held fixed.  Suppose income in our city increases substantially; this would shift the demand curve out, i.e. the market would demand more housing, at any given price.  The intersection of supply and the new demand shows that some new houses would be built (Q1 – Q0 houses) and housing prices would increase from P0 to P1.  Notice that in a market with elastic supply, a lot of housing gets built – Q1 – Q0 is “large,” and P1 – P0 is “small.”

Contrast this with Exhibit 2, a heavily regulated market with fairly inelastic supply.  In this case, the same initial demand shock results in a “large” increase in prices, and a “small” quantity response.  We will return to this theme when examining some housing policies, in later posts.






























Elasticity


Elasticity is economic jargon for "responsiveness."  It's the proportionate change in output given a proportionate change in price.  
Mathematically, we can represent the price elasticity of demand:









There are many elasticities, e.g. supply vs. demand elasticities; with respect to price, income, population…




Thursday, October 12, 2017

Getting Housing Incentives Right: A Classic Case Study from Malaysia



In previous posts, we briefly discussed the problem of the "omitted middle" of the housing market, and the role played by incentives.  I mentioned the classic 1989 study of Malaysia’s housing market by Larry Hannah, Alain Bertaud, Steve Mayo and myself, with input from dozens of Malaysian colleagues. The study focused on the problem of the omitted middle, among other issues.  Nearly 30 years later the specific results of the study are dated and no longer apply. But it remains a good example of a methodology that has been extended and applied in a number of countries.


Why Governments Intervene:  Types of Market Failure


There are a number of possible reasons why private markets might fail to reach an efficient or equitable allocation, giving rise to a potential need for regulation or some other government intervention.  Detailed surveys and discussion of these rationales appear in any introductory text in public economics; see for example Haveman (1976),  Malpezzi (2000), or my class notes on market (and government) failure.  While often presented in texts as mutually exclusive and clear cut, in practice various types of market failure often overlap.

One classic rationale for public intervention is the existence of a so-called "public good."  Economists define public goods quite strictly, and in a sense not used by the general public.  A public good as defined by economists is one where there are no rivalries in consumption of the good; nor can consumers be excluded from consumption of the good, once provided.

A classic example of a pure public good is national defense.  It is generally impossible for a government to defend some of its citizens and not others.  At the same time, individual taxpayers have an incentive to understate their willingness to pay for defense, since an individual either consumes the entire defense package or leaves the country.  Pure public goods in this strict sense are rare in the urban context, and we will not discuss them further in this post.  But some goods can be considered "quasi-public."  While not meeting the strictest test exactly, roads, public schools, and fire and police protection might be considered examples of public goods if we loosen up a little on the strict definition.

Economists also note that it will be difficult, sometimes impossible, to determine consumers’ true willingness to pay for public goods, pure or "quasi."  An important exception, very germane to urban development, is the "Tiebout model" wherein consumers can "vote with their feet" by moving to a location with their preferred set of public goods (and corresponding taxes).

A second source of market failure is the absence of clearly defined and enforceable property rights.   In Anglo-American land use law we often use the useful metaphor of  "a bundle of sticks" for property rights.  These include possession, the right to physically occupy; control, the rights to use and manage; the right to income, i.e. residual returns after paying expenses, mortgages, etc.; security, in the sense of personal safety and the right to avoid involuntary seizure or encroachment; mortgageability, security in the financial sense, the right to borrow against the asset, and transferability, the right to sell or lease the asset, assign it in a will, etc.  The definition of such rights, and clear rules for the adjudication of disputes is a prior requirement for a functioning housing market.

The next classic rationale for public intervention is the existence of a monopoly.  These can be related to a government regulatory regime or other intervention that limits firm entry or exit; other monopolies occur naturally, due to increasing returns to scale.  This is a characteristic of a lot of infrastructure, and is the rationale generally cited in the discussion of public utility regulation (Brown and Sibley 1986).  Baumol (1982) demonstrated that markets could work well even under such conditions as long as the markets were contestable, i.e. that entry and exit were free.  Baumol and Lee (1991) discuss the application of contestable markets to developing country urban contexts. Barriers to entry and exit have also long been recognized as critical factors.  It has also been recognized in the literature that regulation can have the consequence – intended or not – of creating barriers that impede entry into or exit from a market.

Another very broad class of market failure oft discussed in the literature is the existence of large transactions costs.  Demsetz (1968) and Williamson (1975) are among the seminal contributions.  In fact one of the classic reasons for the existence of cities is the reduction in transaction costs, for example by facilitating face-to-face contact.  On the other hand, cities can at the same time increase some transactions costs, such as congestion, although their very existence demonstrates that the latter effects are small relative to the former, at least up to a point.

A particular type of transaction cost much studied in the recent literature is information failure, more specifically asymmetric information.  In plain English, "I know something you don't know."  More formally, AI arises whenever parties to a transaction do not have equivalent information relevant to the transaction.

Zoning, for example, can be partly viewed as a way to control for a lack of information about future nearby developments.  If I build my house on this plot, what's to keep someone from building a leather tannery nearby, or opening a hog farm, or a strip mine (to choose some obvious nuisances).  Zoning or other land use regulations can give me some assurance, if not complete certainty, that at least some nuisances will be controlled.

Ex ante:  AI before the contract or deal is called adverse selection.  A well known type of adverse selection is called the "lemons problem," famously analyzed by George Akerlof as the market for used cars -- how do you know you're not buying a "lemon," a car with hidden problems?  What are the implications for prices and other aspects of market behavior?  Similar problems come into play all the time in real estate transactions, where the buyer may know much less about the property than the seller.  How can the lemons problem be attacked?  Data and analysis can mitigate such AI -- do your due diligence.

Ex post:  AI after the contract or deal is called moral hazard.  Moral hazard is common in real estate development because developers typically receive significant fee income even if the project fails. Another reason moral hazard exists in development is extreme leverage: heads I win, tails the bank loses. Moral hazard can be mitigated using techniques such as recourse mortgages.  More generally, try to design better incentives, monitoring, pay-for-performance.  Will the game be repeated?  If so, reputation can matter.

Yet another major class of market failure, perhaps the most important in the context of urban land use, relates to the presence of externalities.  External costs are costs that are imposed upon parties outside the transaction.  External benefits are benefits conferred upon parties outside the transaction. This important class of market failure will be the driving force behind much of the Malaysia case study analysis below.

Externalities are especially important in housing and real estate. Every real world real estate development imposes at least some costs on at least a few neighbors. Sometimes these costs are substantial.  The location of especially noxious real estate developments have been regulated for centuries, for example the location of tanneries, slaughterhouses, chandlers and the like in an earlier age; the location of a wide range of factories and other industrial buildings are regulated today. Even the best designed residential development generates some additional traffic congestion.  Most new developments will, without some government intervention (or a side deal between developers and neighbors to similar effect) increase the neighborhood’s impermeable surfaces and runoff; and greater demand for education, police, fire and other public services.

Incomplete markets are another class of market failures.  The "missing middle" we've alluded to above and in the previous post is a type of incomplete markets.  When we observe formal developers building only high end real estate, an "informal sector" providing very low quality housing with little if any security of property rights (for "owners" or for "renters"), while a potential market between these goes un-served, the missing middle is a serious market failure.


Possible Responses to Market Failure


What are possible solutions, or ways to mitigate, market failures?  There are five major ways governments intervene in housing markets:

  • Definition and enforcement of property rights
  • Direct public provision
  • Taxation
  • Subsidy
  • Regulation

Broadly, these interventions can often be treated as substitutes.  Certainly they can each be valued, i.e. costs and benefits estimated; and the incidence of the tax, subsidy, regulation or whatever can be studied.  But of course there are other senses in which they are not equivalent.  For example, in the environmental literature there is a large body of work that suggests that in most circumstances taxes on pollution may be more effective than command-and-control regulation (Eskeland and Jimenez 1992).

Government interventions, like any other activity, generate both cost and benefits. These costs and benefits may accrue to different agents or elements of society– developers, landlords, governments, or consumers. Getting the Incentives Right illustrated how to study a wide variety of interventions in a unified framework.  That paper provides an illustration of how, using simple but defensible assumptions, it can be useful to treat a set of taxes and subsidies and regulations as functionally equivalent in order to study their net effect on urban real estate market outcomes.


Governments Can Fail as Well as Markets


In principle anytime we can plausibly posit the existence of externalities or some other market failure, government interventions could, in principle, mitigate, if not eliminate, the market failure. But this is not necessarily what happens in practice. Using government interventions to correct for market failures places a very high demand on the ability of politicians and civil servants to understand the exact nature of the market failure, the related magnitudes of costs and benefits, and careful mechanism design to develop a tax or subsidy or regulatory mechanism that is effective and precise in mitigating the market failure. The world is full of examples of regulations or other interventions that overdo it, that “throw the baby out with the bathwater.”

In general government failure exists whenever an intervention is introduced that does not properly correct for a true market failure, either because no such failure exists in principle, or more commonly, when the intervention is poorly designed and/or poorly implemented. Lack of detailed knowledge about the size and nature of the market failure, and of good mechanism design, is one source of government failure.  Another source is that not every government agent, whether politician or civil servant, is necessarily a disinterested party trying to maximize some theoretical "social welfare function." Complex systems of taxation and regulation subsidy also give rise to opportunities for corruption.

So, what was new, if anything, about the Malaysia case study?  It was based on bringing together a series of individual analyses of different government interventions  Each affects the price of housing; but these various interventions are usually studied one at a time, in isolation.  The model integrated analysis of the costs and benefits, and their incidence, in a simple present value accounting framework.  This particular investigation into Malaysia's Special Low Cost Housing Program showed that many government regulations tended to raise costs, reduce supply and yielded little or no benefit to consumers. The model also helpeds us think about how some interventions interact with each other.

Several decades on, the model developed for the Malaysia study is no longer an accurate depiction of  that market.  Abdullah et al. (2011), Foo and Wong (2014), Shuid (2016) and Sufian and Mohamed (2009) provide more up-to-date discussions of Malaysia's recent housing policies.  Nevertheless, while now dated in some particulars, we claim the approach taken in Getting the Incentives Right remains a useful tool or framework  for assessing whether government interventions are providing developers with appropriate incentives, or are merely roadblocks in the housing market.  Getting the Incentives Right was also an important input into a number of broader policy documents, including the World Bank's (1993) housing policy paper Enabling Housing Markets to Work.

The ultimate goal of the model is to use the present value accounting framework to provide more complete answers to several deceptively simple, important questions about government interventions:

  • What does it cost?
  • Who pays for it?
  • Who gets it?
  • What is it worth...  in the market place?  to society?  to recipients?

As it happens, while undertaking this analysis (and examining similar analyses in several other countries) we often found that the biggest distortion in costs and benefits, the largest efficiency and equity losses, were related to an extreme case of “incomplete markets;” what we and others have termed the "omitted middle."

Looking for Omitted Middles (and Sometimes Omitted Ends?) A Useful Precursor from Paul Strassman


We were not the first to examine the issue of the omitted middle, or the last. We will briefly touch on a few examples.




















Exhibit 1

Exhibit 1, from a classic paper by Paul Strassman (1977), sets out in a very highly stylized fashion a hypothetical description of six housing "types," from temporary and substandard, to good and "luxury,"  Obviously this is a gross simplification since in reality there's a wide range of housing types and costs.  But this approach, while simplified, is much better for our purposes than the commonly used even more extreme assumption, "assume all houses are the same, i.e. just count houses."


Exhibit 2

Exhibit 2, also from Strassman, matches these 6 house types with putative households with hypothetical incomes. Strassman’s stock-user matrix goes beyond the common simplification of a single average representative consumer and representative unit, but it is still limited to a small discrete number of each (six in his example). This simplified model illustrates only ideal matches; there are no off diagonal matches, which would surely be found in any real-world fitting of such a matrix. 

It's a very revealing exercise to study the matches we actually find in survey data.  Richard Green has empirically fit simple matches between housing units and households using U.S. micro data sets, at the state level in Green and Malpezzi (2003) and more recently using the metropolitan area as the unit of analysis as one of his contributions to Schwartz et al. (2016).  Green's work in Green and Malpezzi and Schwartz et al. provide clear demonstrations of the importance of such off-diagonal mismatches, and how best to measure housing "affordability." Theoretical models such as DeLeeuw and Struyk (1975), and Sweeney (1974), are among exercises in analyzing the dynamics of matches between a continuum of housing units and a continuum of consumers. These dynamics are interesting and important but beyond the scope of our simple analysis today. Matching models, some of substantial technical sophistication, have been used for example to optimize the match between medical interns and residents, and hospitals.  See Alvin Roth (2015) for a readable introduction of these and other matching models.


Background to the 1989 Malaysia Case Study


In 1988 Malaysia requested the World Bank to undertake a "sector study" to explain why their formal housing costs appeared to be so high. This was so despite the existence of a Special Low Cost Housing Program designed to induce private developers to build low cost housing. The program was innovative in that virtually all the units were to be built by private developers, using on both private and state-owned land. Developers were permitted to build 60 percent of their units to high standards with high profit margins in order to subsidize the remaining 40 percent. The SLCHP was in effect an early example of "inclusionary zoning." For the low cost units, reduced infrastructure standards and streamlined regulatory approvals were envisioned but these proved to be insufficient. In its first year, the SLCHP fell well short of the target number of low cost housing units it had hoped to build.

The Malaysia Housing Sector Report was the Bank's response to this request. The study had three interlocking parts: (1) an analysis of aggregate market and macroeconomic data; (2) a detailed study of land use regulations and their effects on development efficiency; and (3) a higher-level analysis of the costs, benefits and incidence of a wide range of government interventions.  As is already apparent, this blog posts focuses on the cost-benefit analyses, part (3). In addition to the original report, see Malpezzi and Mayo (1997) regarding the macro analysis (part 1), and see Bertaud and Malpezzi (2000), regarding the land use analysis (part 2).













Exhibit 3

The photo above shows one typical SLCHP project of the era, outside of Kuala Lumpur. These townhomes were built to standards well above those observed in Malaysia’s large informal housing sector, but modest compared to the high end of the market.

 By informal we mean housing built outside the formal real estate development system. The term is somewhat loosely defined, but generally means the units are not built to codes, and generally illegal. Property rights are not as formalized in informal markets, and security of tenure is often precarious. A wide range of terms such as “slums,” “irregular settlement,” “squatter housing,” “favela,” “bidonville,” “shantytown,” “barrio,” or “gekucondu” are used in different places and by different authors as labels for informal settlements of one kind or another. There are huge differences in definitions and living conditions in practice. These and other labels deserve their own post another day, since there is a lot of confusion and some outright falsehood to be found in some of the literature. Angel (2000), Arnott(2008), Payne (2007), Gilbert and especially Mayo et al. (1982) may be profitably consulted on these kinds of settlements.

In the event, the analysis of government interventions' effects was carried out on 14 representative housing units--inside and outside the program, formal and informal, owner occupied and rental, and in different locations-- demonstrated that the costs of regulatory and pricing restrictions far outweighed the benefits of subsidies and regulatory exemptions. In this blog post we will look at just one of the 14 cases, which will be sufficient to illustrate the methods.  The conceptual model used in the study is quite general and can be applied in other countries.


A Simple Model of Regulatory Costs and Benefits


Real estate development involves a number of market participants or "agents:" the economy as a whole, developers, landlords, consumer households, mortgage lenders, neighbors, governments all view a project from their own perspective or point of view.  The model starts with the standard economic cost/benefit analysis of a representative investment, and then adds the major interventions with simple assumptions about incidence.

As we have noted above, government subsidizes, regulates, taxes and otherwise intervenes in housing markets for a variety of purposes.  Each policy intervention can be analyzed in turn by examining how the interventions change housing and/or input prices, translated into corresponding present values.  Present values have the advantage of enabling direct comparisons of the costs and benefits of quite different interventions in different programs.

Some interventions impose costs (e.g., land use regulations, taxes, rent controls, building regulations) on a given agent, and some confer benefits (e.g., land subsidies, tax relief, financial subsidies).  Some interventions confer corresponding costs and benefits on different market participants; for example, rent controls benefit some tenants at the expense of landlords (and perhaps some other tenants). Other interventions confer costs and/or benefits on some participants without an obvious corresponding gain or loss elsewhere.  For example, some very high infrastructure standards can confer large costs on developers without producing much in the way of benefit for anybody.

While there is nothing technically difficult about doing so, it's not as common as in should be to add up all the effects of all the numerous taxes, regulations and subsidies.  In the U.S. the "user cost" literature takes a related approach, usually focusing on the interaction between taxes, inflation, and finance, using the metric of "user cost operators," or capitalization rates in common parlance.  DeLeeuw and Ozanne (1981); Diamond (1978) provide examples. The Malaysia case study, and other examples described below, adopt a variant of the same approach, albeit with a broader set of interventions, and using present values rather than cap rates/user cost operators as the key metric.

In the simplest variation of this framework, used here, we focus on three key entities from whose point of view housing policies and programs are evaluated:  the economy, housing suppliers (or developers), and households.  The exact incidence of the various costs and benefits of government interventions can be a subtle issue.  For example, although the incidence of the property tax appears straightforward _ property owners pay the property tax _ some portion of the tax could be shifted to tenants (for rental property) or to the owners of capital generally (if capital markets were well integrated).  See, for example, Aaron (1974) and McLure (1979).Incidence can depend on the competitiveness of the market, the state of transactions costs and knowledge in the market, the efficiency of financial markets in a country, and the time frame _ in other words it is rarely settled and unambiguous.  Hannah et al. adopt a simple approach, where the entire cost or benefit is assigned to one participant.  If our knowledge of actual incidence changes it would not be difficult to build in a different treatment of incidence.

To anticipate results below, analysis using this model demonstrated that despite strong demand, on balance government regulations still cost the developer money, raised costs, and reduced supply.  Many of these regulations yielded little or no benefit to consumers or anyone else.

The model starts with the standard economic cost-benefit of a representative investment, then adds the major interventions, with simple assumptions about incidence:



The Economy

+ Market Value of the Unit
- Resource Cost to the Economy
------------------------------
Net Economic Cost-Benefit


The Developer

- Resource Cost to the Economy
+ Land Subsidy
+ Development Period Infrastructure Subsidy
+ Construction Subsidy
- Cost of Land Use and Building Regulations
- Land Acquisition Taxes
+ Sales Price
-------------------------------------------
Net Financial Cost-Benefit to Developer


House Purchasers

- Sales Price
- Registration Taxes
- Property Taxes
- Extra Transactions Cost of Program Participation
+ Market Value of the Unit
+ Recurrent Infrastructure Subsidies
+ End User Finance Subsidies
---------------------------------------------------
Net Financial Cost-Benefit to Purchaser



Mechanically, the actual model is built in a spreadsheet. Malpezzi (1988) provides a detailed discussion of the specific spreadsheet models implemented in the Malaysia case study. Decades later the particular spreadsheet models described in that paper are dated. Those models were built in Lotus 1-2-3, and the macros used were the old-fashioned style of storing keystrokes for Lotus. Most users would now build such a model in Excel, and replace the old-fashioned model control with the Visual Basic for Applications (VBA) code that controls Excel. (See Malpezzi 1999 if unfamiliar with VBA. Many Excel users are surprisingly unfamiliar with this built in programming language, which in increases which greatly expands the powers of Excel.)

Even though the actual spreadsheets were built with now-obsolete software, the structure of the problem is unaffected, and a review of the early spreadsheet models that are printed out in Malpezzi (1988) may provide some ideas to users building models today. An introduction to cash flow models in Excel can be found in Malpezzi (2013). Malpezzi (2000) describes how to run a database of alternative cases through a cash flow model and collect results using VBA.

The model is very Graaskampian.  In my famous UW predecessor’s classic ULI paper, Fundamentals of Real Estate Development, he argued for just such analyses of real estate investments from alternative points of view. See Graaskamp (1981) especially pages 3-5.

The model can also be viewed as a generalization of the World Bank’s stylized cost-benefit of a bricks and mortar project, as detailed in Squire and Van der Tak (1975) and Gittinger (1982). That framework also revolves around starting off with observe prices and then subtracting taxes and adding back subsidies to obtain the pure shadow price of the activity to society, not of those government interventions. The main extensions here are to include a wider set of interventions, especially on the regulatory side; to examine the investment from more points of view; and to use the analysis less to focus on the actual physical investment, and more on the effects of the interventions on the incentives to market behavior; and to suggest policy changes that improve these incentives rather than take them as a given.

Many of the inputs to such a model can vary over some range, especially when more than one "representative investment" is considered. But experience teaches that adding up the best estimates of as many significant interventions as possible yields valuable insights into market behavior.  Spreadsheet implementation then facilitates sensitivity analysis, using the "scenarios" capability built into Excel for simple analyses, or writing VBA code or using an add-in like Crystal Ball or @Risk if more elaborate analyses are called for.

In the end, the relationship between these calculations and market behavior is straightforward. If the economic cost/benefit is positive, the unit is an efficient use of society's scarce resources. If the developer's cost/benefit is positive, a supply response will be observed. If the purchaser's cost/benefit is positive, there will be demand for the units.

The analysis can be carried out for different kinds of units, different tenure arrangements, public versus private producers, units in different locations and so on. In each case there are several logical possibilities.  In this case, with 3 "agents" or points of view, that is 2^3=8 possible outcomes. An example of a desirable outcome would be a case where a unit is socially efficient; will be supplied; and for which there is demand. An example of an undesirable outcome is the case where a unit is efficient, there is demand but (say) regulatory costs make it unprofitable to produce such units.

The convention used in this model is that an exemption from a regulation which has an identifiable benefit to society similar to its cost is treated as a subsidy. Reductions in regulations which do not yield corresponding benefits are, therefore, pure cost reductions. In other words, there is a baseline of "normal" desirable regulation from which extra regulatory costs are measured.




Case Study: Present Value Analysis of the 1989 Version of the Special Low Cost Housing Program


Present value analysis demonstrated why developers found the Malaysian SLCHP less than enticing. Even more importantly, the analysis illuminated why costs were so high for formal units outside the program. The Malaysia model, as we refer to it, aggregates results from the analysis of different inputs: land, construction, finance and so on. As always, garbage in, garbage out: the results are only as good as the input studies that feed the aggregate model.



Since the model is a top level integration of inputs from studies of specific inputs; these require additional work, which can be simple but some of which can be substantial.  Here we discuss three: timelines for approvals; the efficiency of land use at the development site; and financial subsidies.


One Key Input: Timelines for Approvals


Analysis of the processes and time required for approvals of new construction or other businesses are not new of course. The best known such study in a developing country is Hernando DeSoto’s (1989) analysis of approvals in Peru, The Other Path. In one famous example, De Soto found it took some 289 days to receive the 11 permits required to open up business and build a factory for basic clothing in Peru.

In another exercise, De Soto’s Institute for Liberal Democracy studied the steps required to legally obtain the necessary approvals for developing unused land. These parcels were typically state-owned, and first required a process of “adjudication” of the land that took 4 ½ years, and 207 separate steps in 48 government offices. After the adjudication process, a development permit had to be obtained from the city Council, followed by the necessary building permits.  All-in, the approvals required 7 years, before actual development could commence.

More recently the World Bank has collected data on theoretical approval times in their annual Doing Business series. World Bank 2017 focuses particularly on building permits.  The Doing Business timelines are theoretical.  DeSoto charted data collected by assistants who sought and timed actual approvals. The World Bank effort charts the steps, then assigns timelines based on the official timeline is built into the laws and regulations; actual timelines experienced in practice can depart substantially from these theoretical timelines.






























Exhibit 4

The housing development process in Malaysia was particularly complex in the 1980s, with something like thirty major steps required in order to obtain permission to develop residential plots.  A flow chart of this process developed by local developer and analyst M.K. Sen (1986).  Sen's chart, reproduced above, has become a sort of cult totem to those working on urban regulatory issues.  The flow chart can be found in Sen's original article, and has been reproduced in Hannah et al.  (1989) and World Bank (1993).  Presented with a Exhibit like Sen's, showing thirty steps required for land development that take seven years to circumnavigate and which can be short circuited at any step, any one can see a problem.  Not only was there a holding period cost, but the additional risk involved in development was substantial.












Exhibit 5

We don’t argue that the optimal system has zero approvals, of course. Real estate development does generate externalities that can be mitigated with judicious land use regulation and other interventions like well-designed taxes or impact fees. The exact details of an appropriate approval process will and should vary with the country’s existing institutional framework. Example 5, from Malpezzi 1998, shows one possible example, in a U.S. style system with separate building permit, zoning, and subdivision regulations. Such a system can easily be navigated in less than a year, providing a decision to land owners and developers, safeguarding against large external costs, and keeping risks of real estate development to a reasonable level. This example is purely illustrative of course; many other reasonable process designs are possible.  Other systems are possible of course.



A Second Key Input: Regulations and Site Land Use


Urbanist Alain Bertaud was a key member of the GTIR team; some of his contributions comprised an application of his eponymous land use model to our case study projects.  Bertaud Model applications stand on their own, but we were also able to use key results from Bertaud's work as inputs to the overarching incentives spreadsheet model discussed herein.

The Bertaud Model is best described as a computer model of land use at the subdivision level, that analyzes the economic cost benefit alternative site plans. The model is built in AutoCAD and a related spreadsheet program.  Bertaud and Malpezzi (2000) provide a conceptual discussion of the model, along with further discussion of this particular case study; but Bertaud Bertaud and Wright (1988) is the best and most detailed explanation of the model itself.

There are, of course, many models of subdivision layouts.  Some models like Erskine and Rink simply maximize the number of lots subject to zoning setback and other regulatory constraints. With the proliferation of more friendly software environments from improved versions of ArcGIS and SketchUp there are many choices for designing a subdivision layout.  Not all have the simple but powerful features of the Bertaud model, which permits the analysis of physical effects of land use (for example the effect of a change in road widths on density throughout the subdivision) but also the economics of a subdivision design.  This can be complex, because in practice (and in the Bertaud Model) willingness-to-pay varies for different plots, e.g those serviced by roads of different widths, with better or worse access to trunk infrastructure, at different differences from parks and schools and other amenities.

Taking advantage of the model fully requires a sophisticated understanding of a range of inputs and parameters. Prices vary with different sizes and different locations vis-à-vis other elements of the subdivision such as the aforementioned parks and schools. Many subdivision models and analyses focus solely on minimizing land costs and/or maximizing the number of plots delivered, subject to regulatory constraints.  In contrast, the Bertaud Model focuses on maximizing value, i.e. the difference between market prices and costs.  Simply minimizing costs can yield suboptimal, even perverse outcomes. The model incorporates pricing zones within the subdivision, helps improve the provision of trunk infrastructure, and optimizes the mix of unit and plot types rather than simply repeating a single average design ad infinitum.

To quote Bertaud Bertaud and Wright, the model “gives planners and engineers the freedom to choose from among a wide range of options. However they do not replace the planners’ and engineers’ judgment. Information about design alternatives provided by the model must be combined with knowledge of local market conditions in order to choose designs which provide a maximum value for beneficiaries at minimum cost.”

In the preceding section, as soon as we view M.K. Sen's flow chart of approvals, we can see a problem.  The Bertaud Model exercise reminds us of the opposite phenomenon:  sometimes costly regulations are complex and subtle.  Consider the output from two iterations of the Bertaud Model, presented here as Exhibit 6.  The left panel presents us schematic subdivision layout under the Special Low Cost Housing Program rules, which were relaxed along several dimensions when compared to prior codes. The right panel is the same subdivision after Bertaud makes two additional suggested changes: eliminate back alleys, and reduce the setback of corner plots.




Exhibit 6

Examine the differences between the left and right panels of Exhibit 6.  When Alain first presented these pictures to me, I didn’t see a huge difference. I’m a dork economist, not a trained planner or architect. Alain, on the other hand, immediately understood the implications. I shared his excitement only once I noticed the FAR, or floor area ratios, associated with each panel. In the first example, a baseline FAR of 0.23 almost doubles to 0.41 after just two changes, a minor change in corner setbacks, and the elimination of back alleys.


An FAR (sometimes called FSI or Floor Space Index) is, of course, the ratio between plot area, and floor space.  An FAR of 1 means that every square meter of developed land is associated with 1 square meter of floor space. FAR may be controlled directly by regulations specifying a maximum (or, less commonly, a minimum) value for FAR; FAR may also be affected indirectly by other regulations governing road widths, setbacks, height limitations, and the like. Details of FAR calculation matter -- what counts as floor space, and whether the calculation is based on a single plot, or a larger development/neighborhood are two important details.  To give some very rough benchmarks for American readers, the FARs reviewed here, 0.23 and 0.41, might be found in U.S. suburbs; an FAR of 1 might be considered "urban" in a small city or town.  Dense areas within Manhattan might have FARs of 10 or above.

Analysis by Bertaud showed that the required setbacks and back alleys eliminated in the right panel of Exhibit 6 were not much valued by consumers, even though they were very expensive in terms of land.  So reducing these regulations had the effect of lowering costs without a significant change in benefits.  Of course if consumers valued set backs or back alleys highly, the analysis would have to be modified to account for this.

Such an increase in FAR with no decline in consumer willingness-to-pay speaks volumes to any potential developer.  More to the point, analysis by Bertaud showed that even the SLCHP regulations reflected in the left panel of Exhibit 6, while an improvement over previous, even more stringent regulations, still tilted profitability away from low cost housing and towards the high end of the market.  Bertaud's suggested regulatory changes such as the elimination of required back alleys and modified corner setbacks tilted profitability back towards the lower and middle ends of the market.


A Third Key Input: Financial Subsidies


The third key input is an estimate of the value financial subsidies. Readings listed below by The Congressional Budget Office (2014), Bertrand Renaud (1984, 1999), Robert Buckley (1997), Loic Chiquier and Michael Lea (2009), Doug Diamond and Lea (1992), and Marja Hoek-Smit and Diamond (2003) discuss financial subsidies in much greater detail than we can provide here.

There are many different ways that governments can subsidize housing finance.  Hoek-Smit (2000), and Renaud (1999) are good introductions to these methods, their analysis, and typical pros and cons.  The simplest, already discussed is an interest-rate subsidy, whether bought-down by governments, or lowered by fiat.  Another method is the so-called soft second mortgage, where government or a regulated institution defers principle and subsidizes interest.  Buy-down mortgages are another variation of the theme of reducing monthly payment over a fixed period of time.  Blocked-deposit mortgage or consumer loans require borrowers to first place a required deposit, typically at below market returns, to be used in case of late payment.

Up-front grants/allowances can be included as part of a housing financing package; these can be applied towards deposit and closing costs, or towards mortgage loan payments.  Savings towards down-payments can be subsidized.  Mortgage/consumer loan insurance or guarantees for top part of loan or to insure against other risks.  Community mobilization/ individual counseling programs are less often thought of as subsidies, but to the extent they have value and are provided freely, they can be considered as such.

In the event, computing the most obvious and common subsidy, an interest-rate subsidy, is analytically simple, though finding good data -- the un-subsidized market rate, and the expected holding period of the loan -- may be problematic.  Assuming a below-market fixed-rate mortgage, compared to an un-subsidized markets benchmark, we simply run out the cash flows of the subsidized mortgage for the expected holding period, and then discount them back at the market mortgage rate of interest.  In the U.S., where prepayment is common (and endogenously determined partly by the time path of interest rates), holding period is a little hard to pin down, but in a context with a deeply subsidized mortgage and/or no free prepayment, as in Malaysia, this is not much of an issue.  The more difficult part was finding a benchmark for mortgages.


Adding Up the Effect of Multiple Interventions


Here we'll recap, and summarize some of the key results form one of the 14 Malaysia case studies.

Some SLCHP developments received land below market cost from individual states; others received land at closer to opportunity cost or used private land. For this particular representative example, we assumed that state land was used for a nominal charge. The difference between fees charged and estimated market land value yielded a land subsidy of about M$8,000 to the developer (at the time, the approximate exchange rate was 2.5M$ = 1 U.S.)

Charges for infrastructure connections were about M$1,450 below their estimated cost. A small cement subsidy roughly canceled cement prices above world market prices, and no other significant construction subsidies were found. But the developer land and infrastructure subsidies were largely offset by other interventions. The costs and benefits of land use regulations were estimated using the Bertaud Model of land use site plans. The model, described further below and in detail in the references, compares the cost of development under current regulations with the cost of development under some regulatory baseline, adjusted for any benefits which may accrue from the additional regulation.

In 1988, Malaysia land use and infrastructure standards were particularly high. The SLCHP produced a new set of lower standards for low cost housing. However, the actual approval of plans utilizing the new standards were given by local authorities who at the time continued to rely on previous, higher standards.

The Bertaud Model analysis of land use demonstrated the following regulatory costs:

1. The reduction in salable land from numerous requirements for road widths, setbacks and large set-asides for public areas. The estimate of the cost of land use and infrastructure regulation over a baseline of "reasonable" outcomes was M$6,000 per plot; that is, the difference in cost between the current standard (as little as 25 percent salable land) and the recommended standard (65 percent).  This is a conservative estimate. For example, by lowering road design standards, salable land could have been increased, but surfacing and maintenance costs would also have been reduced. The latter potential cost savings were not included.

2. The delay imposed by regulatory procedures which tie up capital and increase risk. In the United States, for example, developers often take a year or more to receive planning permissions. But in late 1980s Malaysia, as we saw above, the timeline of approvals averaged about 7 years.  Given estimates of the average delay (compared to some reasonable baseline), the amount of capital tied up, and its opportunity cost, estimating this cost is straightforward. In the example, we used a conservative estimate of M$1,000 per plot.

3. Controls on sale prices are a regulatory cost to the developer but a financial subsidy to the purchaser. The nature and size of the transfer depends on location, since sales prices vary less than market values with location. In this particular example the unit was estimated to be worth about M$30,000, but the sales price was M$25,000, implying a transfer of M$5,000.

4. Other costs to developers include building codes and standards (judged not large in the present case, since these codes seem roughly in line with the market) and regulations encouraging sale to ethnic Malays and indigenous peoples which are costly to comply with, especially in some urban areas with high Chinese and Indian populations. Compliance lengthens the developer's holding period, and frequently discounts have to be offered to reach the desired mix. These regulations were estimated to add M$1,625 to developers' average unit costs.

Taxation of housing in Malaysia was fairly light. The main taxes for sales programs comprised acquisition taxes, assumed born by the developer, and property taxes, assumed born by the purchaser. Capital gains taxes were levied on nominal, not real appreciation, on a sliding scale depending on the holding period. In this example we assumed the unit was not resold, and that program units were exempt from transfer taxes; so no transfer tax was paid.

Despite the light tax environment in late 1980s era Malaysian real estate, we suggested that more work on taxation would be high on the agenda for future model development.

Financial subsidies were estimated by calculating the present value of the subsidized cash flow at unsubsidized rates. In some countries with poorly developed financial markets or particularly inappropriate terms (for example, 30 year fixed rate instruments) a baseline unsubsidized rate may not exist; a range of estimated market rates can then be tested. Fortunately in Malaysia's well developed financial system reasonable comparators existed, making the calculation of the financial subsidy straightforward.

Financing was analyzed on the basis of fixed rate self amortizing mortgages. For this example, we assumed a 25 year loan, a market rate of 12 percent (a conservative assumption--the market rate for such a loan could be as high as 14 percent), a nominal lending rate of 10 percent and a loan to value ratio of 0.95. To calculate the present value of the financial subsidy, deflate the nominal principal and interest payment in real terms, then take the present value of the initial loan followed by the real repayment stream, discounted by the market rate of interest.



Summary of the Incidence of Incentives


Market valuation of units was straightforward in Malaysia, where there was an active resale market; economic cost was arrived at by subtracting net regulatory costs from financial cost. In the absence of any government interventions, we estimated that the unit would cost about M$28,100 to develop, but be worth about M$30,000.  Thus developing these units was an efficient use of Malaysia's scarce resources.



Exhibit 7

Government interventions -- taxes, subsidies, regulations, etc. -- drive a wedge between social cost-benefit, and the cost-benefit from the point of view of other agents.  The first figure summarizes the net incentives and "disincentives" faced by developers of a representative SLCHP unit. The developer receives substantial subsidies through low cost land and reduced infrastructure standards.  These are more than outweighed by the costs of "excessive" regulations over our baseline, and the pricing restriction that effectively requires the unit to be sold below cost. The net effect of these interventions is to add about M$4,000 to the developer's cost (the bottom bar) leading to a net loss on each unit of about M$2,000.  Developers won't build, in such cases, unless there is some reason "outside the model" to do so, e.g. a response to political pressure, a public developer not motivated by profit motive, or possibly under the table payments that evade price controls.





Exhibit 8

The "wedge" between social cost-benefit, and an agent's cost-benefit, can go either way.  The second figure tallies the incentives and disincentives to purchasers of a representative SLCHP unit. The estimated subsidy to the purchaser of nearly M$9,000 is mostly due to below market pricing restrictions and mortgage financing. If and when developers would build such units -- for example if in response to political pressure, in order to receive other approvals (which we could, in principle, model if we had more data) there would strong excess demand for such units.










Exhibit 9

Exhibit 9 shows how these costs and benefits add up from the point of view of the economy, the developer and the purchaser. This particular unit is efficient, in other words, it benefits the economy more than it costs it.  Demand would be strong in the absence of additional purchaser incentives, but would be very high given the additional subsidies involved. But because of regulation, developers lose money, so they would build these efficient units only if forced to do so (for example, to obtain planning permission for other units) or if purchasers paid higher than official prices. In Malaysia, the former predominates but both mechanisms can be studied with such models.


Again, this discussion and Exhibits 7 through 9 applied to only one of the 14 Malaysian case studies analyzed.  Analysis of units outside the program was equally instructive in demonstrating how regulations hamstrung developers and ultimately consumers. Analysis by location revealed that administrative pricing led to excess demand in some areas, and costly inventories of unsold units in others.

One of the key takeaways is that often such regulations taxes and subsidies when analyzed in a unified framework demonstrate how government interventions can inadvertently or not tilt profitability away from the middle of the market and/or the bottom of the market and towards the higher end.



Two Extensions, in an Application to Turkey


A decade after the Malaysia case study, Baharoglu, Hannah and Malpezzi (1997) undertook an exploratory study of Turkey’s housing market that included some interesting extensions to the Malaysia model.

Once again, we present a case study from decades ago that at best only partly reflects conditions in Turkey's housing markets and policies today.  More current discussions can be found in Balabou (2012), Ozdemir (2011) and Baslevent and Dayoglu (2005).  Once again, we don't want to focus too much on specific numerical results; rather, this older case again provides methodological insights that can be applied today.

Land and housing supply in Turkey underwent drastic changes during the 70s, 80s and 90s. The formal authorized housing sector boomed from the 1960s until 1980. However, housing development at the periphery of cities was restricted since the corresponding supply of planned and service land was inelastic. That early housing boom mainly took place on the existing infrastructure through increased building densities.

After 30 years of dense high-rise development, urban land within major cities was largely built up, and the early 1980s witnessed a crisis in the housing market. In the 1990s a different course of residential development was undertaken. Large tracts were opened at the urban fringe for mass housing projects. In the earlier infill development, the majority of land was owned and supplied privately in the form of small plots, and development undertake by small scale developers called yap-satsci.  In the 1990s, authorized land supply and housing development at the urban fringe was increasingly dominated by the public sector. At the same time, the unauthorized housing stock, called gecekondus, continued to grow, predominantly on public land, at the periphery of cities. This unauthorized housing stock served as much as 60 percent of the population of Turkey’s major cities.

So despite an increasing public role, most low-income urban households continued to live in private accommodation.  At the time of the study, rents and house prices were increasing much faster than incomes. Government proposals for assistance to low-income households focused on interest-free loans for housing and service lot programs with five years repayment periods. Yet due to the lack of resources those programs were perceived to be functioning poorly. Government programs were failing to reach low income households, particularly those below the 40th percentile of income.

In response, in 1997 Baharoglu, Hannah and Malpezzi carried out a preliminary sketch of a "Malaysia-style" analysis of the Turkey housing market.  The fist author was deeply familiar with Turkey's economy and housing market, and the second author had some experience of Turkey; my role was more on the technical side of model development.  The study was an exploratory draft and never finalized; nevertheless, from an methodological perspective it is instructive along several lines.

In the Malaysia case study we focused on three points of view: the economy, the developer, and the consumer. But there are other actors in the development process, notably government. Governments also face cash flows; taxes and other revenues come in, and expenses such as spending on roads and local public services go out. Mechanically it’s simple to add a few lines to the cash flow model to compute these explicitly. Of course, actually accurately estimating and forecasting these cash flows is can be difficult. In  their preliminary “exposition of concept” study of Turkey, given the short time available, Baharoglu et al. used back of the envelope calculations based on conversations with just a few market participants.

To sum up, the "Turkey Model" and the "Malaysia Model" had many common elements.  They have a broadly similar structure, but in Malaysia we had more time to interview planners, developers and other agents; the Turkey exercise was to motivate a later effort that was not, in the end, undertaken, so there the inputs were “quick and dirty” albeit still informative qualitatively.  The Turkey effort featured a better breakdown of incentives to government, and a better treatment of consumer’s cost-benefit, that allowed us to calculate the benefits from both a financial perspective (valued at market prices) and an economic perspective (based on models of consumer’s surplus).  In the latter, the consumer surplus model allows us to measure how participants might value the housing received differently than the market.



Incentives to Government


In Turkey, as in Malaysia, the model starts with a list of interventions and their costs and benefits from the point of view of the economy, the developer and the house purchaser.  Except for a few details the top line list of interventions for these first three points of view are so similar to the list above for Malaysia that we won’t repeat them here.    But after the line “Net Financial Cost-Benefit to Purchaser” we add another line “Net Economic Cost-Benefit to Purchaser” than adjusts the financial benefit for possible losses due to the fact that the purchaser doesn’t get to freely choose the size or type of unit, or its location.  Details of this adjustment are discussed below.

The other innovation is to compute another explicit set of costs and benefits from the point of view of Turkey’s government:

Government

+ Taxes
+ User Fees and Administrative Charges
- Subsidies Paid Out
- Opportunity Cost of Implicit Subsidies
---------------------------------------------------------------
Net Financial Benefit Cost-Benefit to Government


We could expand the government section further. Government is not, in practice, a monolith. We might expand the treatment of government to consider national state or provincial and local governments separately. There may even be cases where it’s instructive cash flow separate line items for different agencies of a local government. If different levels or agencies within a level face different economic incentives, and several of these agencies have veto power over development, it’s easy to imagine scenarios where a given government agency would veto or at least oppose a development that might satisfy other broader cost-benefit criteria.

As before if the economic cost-benefit is positive (benefits > costs)  the unit is efficient; if the developer’s cost-benefit is positive a supply response will be observed; if the purchaser’s cost benefit is positive there will be demand for the units. The new wrinkle is that if the government’s cost-benefit is positive, the development will have a positive effect on the public purse. Among other things this will affect incentives for necessary government actions, like providing the required approvals and the delivery of infrastructure.

There are several value-added taxes as well as title duties and recurrent real estate property tax.   The subsidies stem primarily from controls on prices, and below-market financing.  Details of these taxes and subsidies can be found in Baharoglu, Hannah and Malpezzi.


Financial versus Economic Benefit to Consumers


Economists often refer to measures of the benefits of consuming a certain amount of a good as willingness to pay, is consumer's surplus.  Economists refer to programs that provide assistance in the form of things (like apartments, or medical care) rather than money as "in-kind transfers" (as opposed to money transfers).  It follows that consumer's surplus provides a measure of "bang for the buck," i.e. how much consumer's welfare increases (or decreases) as a result of providing some good or service rather than money income.

The common sense behind the benefit or consumer surplus calculation is easy to understand: it is the amount of money a typical household would accept as equivalent to participating in the program (i.e. to receiving the in-kind transfer).  The best way to explain this further is to use an example.

Suppose a public housing program offers a government owned 120 M2 apartment to a low-income household with a monthly income of 40 million (MM) Turkish lira (TL), at no charge.  [We're using prices in end-of-1995 Turkish Lira (TL); The December 1995 exchange rate was $U.S. 1.00 = TL 56,000].  Suppose the market rental equivalent was (say) 15MM TL per month.  But many — perhaps nearly all — low-income households would rather have 15MM TL per month in cash and fend for themselves in the housing market, than have a 15MM TL apartment for free!  On the other hand, if they are not given such a choice, but only allowed to either take the apartment or get no assistance, many if not most would probably take the apartment.  That is, they attach some value to the "in-kind" assistance; therefore we know from common sense that the value is greater than zero but less than the market value of the unit. It turns out that given housing demand parameters (the fraction of income households spend on housing, and how that fraction changes as incomes and prices change), and the income of the target household, economic models allow us to calculate this value.

This calculation is discussed in detail below.  Suppose for the moment that we carried through such a benefit calculation, and discovered that the target low-income household valued the benefits from the program at 10MM TL.  That is, the typical low-income household would (we estimate) have a hard time deciding between taking the free apartment or taking 10MM TL per month in cash.  Then 10MM TL is our measure of the benefit of the program.

Once we have calculated the benefit, we can also talk about the "transfer efficiency" of a program.  Consider our made-up example again.  The household receives a benefit worth 10MM TL but the program costs 15MM TL, so benefits are about 67 percent of the costs.  In microeconomics jargon, the transfer efficiency of the program is 67 percent.


Why do we use the market rent of the apartment as the measure of the cost?  It's the opportunity cost.  The government could, for example, rent the apartment out to a higher income household for the market rent of 15MM TL and then use the money to assist low-income households.  In this particular example, government could (if it chose this course of action) help half again as many low-income households the same amount by doing this.

Note that we’ve control constructed this stylized example using monthly rents. It’s no great trick to carry out similar calculations using values (asset prices), as we will do in the next section.


Summary Results from the First Turkey Case



As in Malaysia, we developed a spreadsheet model to analyze the costs and benefits of different housing programs from the point of view of different agents in the housing market. We identified seven representative investments, located at the outskirts of Ankara. Here we look at the first case. That’s a public organization’s project, developed on land partly owned by the state prior to development and partly expropriated from private landowners. Summary statistics of costs and benefits from each point of view are presented in Exhibit 10. The next few paragraphs summarize the results behind that Exhibit, which are detailed in the parent paper.

In the absence of any government interventions, our initial estimate is that a representative case a single unit costs about 1.24 billion TL; our appraisal was that at market prices the unit was worth just a little less, about 1.2 billion 1995 TL. Given the rough approximations necessary in this initial effort, this 3½ percent difference could be considered a moderate loss; an alternative interpretation would be that until data are further refined, we consider the “pure” economic cost-benefit as close to a wash.

The developer received substantial subsidies, mainly land worth over 600 MM TL per unit more at market prices than participating developers were charged.  These subsidies were eroded, but not completely eliminated, by taxes and administrative costs especially a pricing restriction that limited the sales price to 420 MM TL less than market values. Given the land subsidy on the one hand, and the price control cost on the other, the developer more or less breaks even in this case (the difference is less than 1 percent of market value).  On balance, it’s a bit close, but for the moment, we'll call it break-even.  Since market values include a normal return to the developer, building such a unit is an efficient use of society’s resources, and developers will have incentive to supply them.

From the point of view of the purchaser the estimated extra benefit from associated financial subsidies was a hefty 752MM TL, partly due to below-market pricing restrictions and partly due to highly subsidized mortgage finance. New low-income units of this type are also exempt from property taxes.

However the measured benefit to low income consumers is greatly reduced when the unit is valued from the point of view of the target household rather than the market. Net consumer incentives fall from about 752 million to 555 million when the unit is valued using a low the target low income households estimated willingness to pay. While the price per square meter came in low the unit is so much larger than the household’s desired size, even at this reduced price, that the benefit to the household, while still positive, is only 53 percent of the cost of providing the unit at the controlled price.

From the government’s point of view little is obtained in terms of taxes or fees. On the other hand the government pays large implicit land and financial subsidies. This means that every unit imposes a substantial cost on government and reduces the replicability of such a program. The general issue of the trade-off between very deep and costly subsidies and replicability at scale as discussed in Cohen (1983).

The figure shows a gap between economic and financial consumer benefits due to a poorly designed in-kind transfer. We could realign or relocate the project; or more broadly rethink the approach more broadly for example from bricks and mortar subsidies to some form of housing allowance.























Exhibit 10


In this exploratory study of Turkey, it appeared that such projects were socially desirable, according to the preliminary cost benefit of the developer and of the economy.  But the benefit to recipients was greatly eroded by the poorly designed details of the in-kind transfer.  Thus the analysis suggests a property tax or impact fee or some other mechanism might be an effective way to bring incentives into alignment. Of course, the model would have to be rerun since these taxes or impact fees would have to be paid by some combination of consumers and developers.



Critiques of Getting the Incentives Right



The Malaysia model was not without its critics. Baken and van der Linden wrote a comprehensive critique of Getting the Incentives Right, and the World Bank’s housing policy paper Enabling Housing Markets to Work that in part flowed from GTIR. As I noted in my reply to Baken and van der Linden, we actually had more points of agreement than disagreement. Reading the two TWPR papers sequentially is a good exercise. I think the biggest difference between us was our differing views of the costs of informality. Baken and van der Linden write as admirers of the vitality and accomplishments of informal markets, and are concerned that formalizing these markets will have losers as well as winners. I don’t totally disagree, but I suppose I place a higher weight on the benefits, especially in the long run, of formalizing markets. Read both papers and judge for yourself.

Other critics and we ourselves have noted that the Malaysia model as illustrated here and related examples are partial equilibrium models. That’s perfectly appropriate for projects that are small relative to the entire market. But some large-scale it would be important to consider general equilibrium effects, as market prices changed in response to large projects and/or significant public policy actions. Indeed in many cases the goal is to bring down prices even as this reduces the ex post calculation of the return to selected projects as we’ve argued elsewhere.

One could also refine many of the particular input; for example, anyone who had a graduate course in public economics might suggest some alternatives to our simple Marshallian version of consumer surplus used in the Turkey model (Hausman 1981, Schwab 1985, Malpezzi 1998).

But perhaps the most telling critique is that the Malaysia model has not yet been proven to fully meet the “market test.”  The study, and its progeny in several countries (Turkey, Korea and Botswana) often receive favorable mention, and also some criticism.  While all of our discussion has been in the “developing country” context, these models are just as relevant to developed countries, most assuredly including the United States. But to be frank, the number of similar follow-up studies and extensions have been disappointing. Why are these incentive models more talked about than carried out? Exercises like this might not be attractive to some academics because, while complex to execute, they don’t really break any new ground in theory or econometrics, and aren’t easy to publish in a “top journal.”  More importantly, I suspect the main reason is that they are not attractive to applied economists in institutions like the World Bank because of the significant resources required in an environment where urban research, both basic and applied, are not as strongly supported as they were several decades ago.




Final Thoughts


Results such as these can be used to suggest policy responses to rationalize the incentives.

With these four points of view there are 2^4 = 16 logical alternatives.  Remember that when all incentives are aligned, good outcomes occur. If all four cost- benefits from the four points of view of the government, the consumer, the developer, and the economy are positive, or all four are negative, then all incentives are in line.  If all four are positive, it’s a good use of society’s resources, developers want to build it, consumers want to buy or rent it, and governments want to approve it.  That’s obviously a good outcome. But it’s also a “good” outcome if all four are negative:  it’s not a good use of society’s resources but nobody wants it, nobody builds it, and governments won’t try to talk developers into it.

It’s the “mixed cases” where all incentives are not in line that can present difficulties.  For example, if it’s a good social investment, with strong consumer demand (as in Malaysia) but some combination of developers and/or governments lose money, then the supply response will be inhibited. On the other hand, if three of the four net cost-benefits are positive, but the consumer’s net cost-benefit is negative, then demand will be deficient.  Obviously, if an incentives analysis reveals a lot of mixed cases, the next step is to use them as a guide to modify government interventions appropriately, to bring them in line.

One of the side benefits of a Malaysia style incentives analysis is that it encourages the analyst to take a broad view of the housing market, consult a comprehensive set of sources from both public and private sectors, and speak to a wide range of actors in the development process. The analysis will lead you to connect not only with the government officials and academics who are so often the natural counterparts of the typical external “expert.” Sorting out the information required for such a model will lead the analyst to detailed conversations with developers, landlords, brokers, lenders and of course representative consumers, the ultimate beneficiaries of good analysis and effective policies.

And it's the housing consumer who's our ultimate target. As Adam Smith memorably noted, "Consumption is the sole end and purpose of all production; and the interest of the producer ought to be attended to, only so far as it may be necessary for promoting that of the consumer."

Perhaps the greatest benefit of building these models is that as you think of how to build the model and its inner workings, you begin to focus less and less on "what's the answer? what's the number?' and more and more on "how does the market work?"

I’m not a practitioner of Zen, nor am I especially knowledgeable about it. The one book I and many other dilettantes have read is Eugen Herrigel’s Zen and the Art of Archery. Full disclosure: that book itself was later heavily criticized by Zen scholars such as Shoji (2001). Be that as it may, something in the book connected with me as I carried out my share of the Malaysia analyses; perhaps because in my youth I was an archer (albeit a terrible one, as the holes in my parents’ garage behind my target demonstrated). Anyway, in analysis and model building, as in archery, it’s a mistake to focus on the target before you learn the proper form, before you learn how to construct, properly parameterize and verify that what you model is a useful representation of reality. To sum up:

"The right art," cried the Master, "is purposeless, aimless! The more obstinately you try to learn how to shoot the arrow for the sake of hitting the goal, the less you will succeed in the one and the further the other will recede." (Herrigel, 1953)


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