"Classics Illustrated:" A blog post on economics and elections, from 2010

 

As I note in the directory to my blog posts at Wisconsin, Rutgers, and here, Wisconsin has been deleting my blog posts (who wants to read something that's 5 or even 10 years old?)

As I find these "Classics Illustrated" posts, I will post them here, mainly without much of an update.

Here's the first one I've found, about elections, from a decade ago.

Monday, November 1, 2010

Economics and elections

by Stephen Malpezzi, Professor and Lorin and Marjorie Tiefenthaler Distinguished Chair in Real Estate

One of the goals of my urban economics classes is to demonstrate how we can use economics, and data analysis, to understand a range of events in real estate markets, in cities, and in our society more broadly.

Tomorrow's midterm election provides us with a great set of teachable moments. I'm using the effect the economy can have on the election to illustrate some basic techniques of data analysis, critical thinking, and "storytelling;" and, as always, how "Reading for Life" can help us make sense of the world.

Today, I'd like to share just a few of these points, focusing less on the data analysis techniques and more on some interesting stylized facts and research results (mainly results from other people's research).

While much debate surrounds causes, timing, and attribution, the objective fact is that, by some measures, the economy is in the worst shape since the Depression. The next two figures show how two indicators, growth in GDP per capita and inflation, fared during the terms of the postwar presidents since Truman. (The data go back to 1947 and so the early part of Truman's term is omitted. The data run through Q2 of 2010, so the last few months of the Obama administration are also omitted). Let me start with two indicators that previous research has tied to electoral performance.

FIGURE 1. President Obama, so far, has faced a lower growth rate of GDP per capita than any other postwar president. Of course, it's early days, and whatever our partisan leanings, we all hope for better performance in the next two years. Nevertheless, the anemic performance of GDP growth is a challenge for Democrats (who, of course, also control the House and the Senate, at least by the simple definitions of "control.")

FIGURE 2. On the other hand, Obama has held office during a period that's exhibited lower average inflation than we've seen during any other postwar President's term.

Are Presidents responsible for “their” economic averages?

A huge body of research argues the effects of economic policies (taxes, subsidies, deficits, regulations…) and Presidents (and other politicians) do affect these policies. However, the economy has a lot of inertia (lags) built in, and there is a lot of luck involved. (Luck, of course, can be good or bad.) Policies have their lags, too. The economy can react to the perception of future policies and uncertainty in the same. But fair, or not, there is a lot of evidence that election outcomes are affected by the performance of the economy, even over short periods.

Economist Ray Fair (Yale) has published several papers and a book about how to forecast U.S. elections according to the state of the economy. Recently he extended his work from Presidential elections (as in his book) to House elections, in “Presidential and Congressional Vote-Share Equations,” American Journal of Political Science, 53(1), January 2009, pp. 55-72

Ray Fair’s prediction of this week’s Congressional election

Fair’s model has three equations: for the Presidential vote, the “on-term” House vote, and the midterm House vote. The economic variables are derived mainly from growth in GDP per capita and inflation. Other variables include whether there is a Presidential election, and if there’s a war on. Fair’s latest forecast (10/29/10) is that the Democratic share of the House vote will be 49.2%, i.e. a razor-thin Republican majority. He doesn’t forecast Senate results.

What about unemployment?

Fair’s model shows the House vote as closer than most political pundits. The two main economic drivers in his model are GDP per capita growth and inflation. Current low inflation numbers are helping to keep it close. I think the other thing this model misses is our high unemployment and its extraordinary average duration. See the next two figures:


These are even worse than might be expected from our recent growth in GDP; see for example the analysis by the Federal Reserve Bank of San Francisco. High unemployment, and high duration, and how they are now driving foreclosures, are subjects my colleagues and I have discussed elsewhere.

Debate will continue on the efficacy of the policies of the Administration and Congress; between Republicans and Democrats; and the debate that’s always on within the parties.

Despite my PhD in economics, I’d never argue that elections are only about my favorite subject.

But objective data, and past research on elections, show that the state of the economy has an important effect on the electoral fortunes of the party in power. Fairly or not, economic conditions favor the Republicans this time around.


More "Reading for Life"

• Fair, Ray C. Predicting Presidential Elections, and Other Things. Stanford University Press, 2002.
• Gelman, Andrew. Red State, Blue State, Rich State, Poor State. Princeton University Press, 2008.
• Tim Besley's 2002 Lindahl Lecture, Principled Agents.
• William Easterly, Michael Kremerb, Lant Pritch and Lawrence Summers, Good policy or good luck? Country growth performance and temporary shocks. Journal of Monetary Economics, 32(3) December 1993, pp. 459-483.

Cities and the Pandemic: Observations and Scenarios

 

Times Square, New York City: April 2020

Regular readers of my blog -- both of you? -- know that in March of 2020, I began a “teaching library” of PowerPoint slides, with notes, and other materials, about the SARS-CoV-2 pandemic.  I update that rather large set of materials about once a month.  Since the PowerPoint slides currently number somewhere over 1200, it's easy to get a little lost (though as you'll see, they are organized by topics).

As an urban economist, who's focused quite a bit on housing as well as real estate, transportation, finance, and governance, the future of cities after the pandemic is of intense interest.  Recently I gave two related presentations on this topic.  I've combined those two presentations, and originally it came in at a very manageable 200 slides or so.  It's grown just a bit when I added some slides that had a bearing on some of our discussions during and after the presentations, and some resources and references.

You can download the combined presentation here.

The PowerPoint version clocks in at about 100 MB.  If you are using a phone or a slow connection, or have any other difficulties, here's a pdf version.  The PowerPoint version is much preferred, since it has a number of explanatory notes, and lots of references, attached, that do not appear in the pdf.

Like the larger aforementioned "teaching library," this is a work in progress.  I have already received a number of comments from colleagues, which will be reflected in future versions.  This version, dated October 27, 2020, will be updated from time to time.

To be specific, these slides are based on presentations to the 1818 Society’s  Transport Thematic Group and Urban Thematic Group.  (The 1818 Society is the alumni organization of the World Bank Group.)  The presentation is organized as follows:

• Urbanization, economic development
• The COVID-19 pandemic
• How will the pandemic affect cities?
• Discussion: possible interventions
• What does the future hold?
• A few slides touching on our comments and discussion
• Going deeper: some resources

As always, comments and criticisms, and especially corrections, are always welcome.

By the way, the photo of Times Square nearly empty during New York's lockdown is not my vision of the future of New York, or other large cities.  We see "through a glass, darkly" on several points, and there will be some significant changes, but I argue that cities like New York or Paris or San Francisco or Seoul will not be emptying out.

The Coronavirus at the University of Wisconsin: Two Presentations

 

As regular readers of this blog know, since March 2020 I've been updating a large "teaching library" on the coronavirus pandemic, including how it affects cities and real estate markets.  This material is updated every 2 or 3 weeks.

That library is well over a thousand slides, and a bit much for some readers, so from time to time I'll post some more focused selections from the library.

I've already posted a presentation to Wisconsin's Graaskamp Center graduate students on Bayesian thinking, in general and as applied to analyzing the outcomes from a coronavirus test.

At the end of August I provided some UW colleagues a selection of slides focused on some of the problems the coronavirus poses for universities in general, and Wisconsin in particular.  On October 19 I made a virtual presentation to UW's "Sifting and Winnowing" fall panel on "Urban Development under COVID-19."

You can download the PowerPoint versions of the notes on university challenges here; and the slides from my presentation to the Sifting and Winnowing panel here.

For those with limited download capacity, here are some lower-resolution pdf versions here and here.  The PowerPoint slides have a number of associated notes and links that aren't in the pdf version.

My policy is to make these and other teaching materials freely available for others to use in their own classes and presentations.  A brief acknowledgement is always appreciated, as are comments or corrections.

Thinking about Thinking: Are You a Bayesian?

 

Are you now, or have you ever been -- a Bayesian???

One of my long-time teaching tropes has been "thinking about thinking," in which I discuss how economists think, how normal people think (by normal, I mean non-economists of course), some tricks and traps in economic thinking, some lessons from psychology.  But one of the best-loved segments, according to a scientific sample of both students who've come to my office to tell me this, is my discussion of how to be a (mostly "informal") Bayesian.

By popular demand, and by that I mean one person, my friend and colleague Joe Walsh, I'm posting my notes on Bayesian thinking here, for students and others who might be interested.

In this Power Point deck I discuss very briefly, and informally, the relationship between probability and "truth;" and review some basic material from your first stats course on Type 1 and Type 2 errors.

Armed with these tools, we then delve into the three kinds of people in the world:

• People who divide the world into three kinds of people;
• People who don't do that; and
• People who don't care, one way or another.

Then we tackle another tripartite division:

• "Classical" thinkers, a.k.a. "frequentists;
• Ideologues; and
• Bayesians.

Most of our discussion of Bayesian approaches is very loose and informal.  However I do go through the basics of Bayes' rule for decision making.  In teaching, for years I used examples from medicine, i.e. how to think about test results for breast or prostate cancer, in light of Bayes' rule.

Bayes' rule leads to some results that initially appear counterintuitive to many people, e.g. that a woman who gets a positive test for a mammogram using a test with 20 percent false positives may be somewhat unlikely to have cancer.  (Though she's maybe 8 times more likely than someone who tested negative!)

Having gone through that example, traditionally I then went back to discussing the importance of the broader Bayesian approach to problems, namely:

• Starting with an explicit statement of one's prior belief;
• Discussion (with oneself as well as maybe others) of how strong this prior is, and where it came from;
• Whether the strength of your prior is really justified, given its source;
• And how you choose to update your prior, in light of new information.

Getting back to Bayes' rule, before I posted this, I reflected on the fact that this rule is extremely relevant today, when we are all thinking about the coronavirus, including who should be tested, how often, and what it means to get a positive or a negative test.  So I added a discussion of testing for COVID-19 in this framework.  For no extra charge, I built a little spreadsheet model that allows you to calculate the probabilities that you have the virus, if your test is positive, or negative.  

You will find that the results depend on three things:  the specificity of the test (Type 1 error to a statistician), the sensitivity of the test (Type 2 error), and (often most critically) on your prior belief on how prevalent the virus is within the population under study.  Don't believe me?  

Click here for the PowerPoint presentation.

Click here for the spreadsheet model.

And as many readers of this blog know, I've been obsessively collecting detailed teaching notes about the virus, which you can find here.

One more thing -- a short discussion of the coronavirus at UW, created in late August, can be found here.

Cost-Benefit Analysis: Some Notes for Urban Projects

In July 2019, the Marron Institute of the New York University hosted a workshop on cost-benefit analysis (CBA) for 25 urban officials from around the world

I was pleased to be asked to meet with this diverse group and discuss how cost-benefit analysis could be used in decision-making about urban projects.

You can download the PowerPoint from the sessions here.

These slides cover the basics -- time value of money, investment criteria etc. -- as well as applications to housing, and transportation.  The geographic focus is broad, as befits the interests of our group.

References and readings for further study are also included.

Global Perspectives on Real Estate and Urban Development in a Time of Stress

The 2020 John M. Quigley Medal Lecture to the American Real Estate and Urban Economics Association

In January 2020 I was honored, and humbled, to receive AREUEA's John M. Quigley Medal.  I've described the award, and other awardees to date, in a previous post.  Yet another post describes some of John's life and work, and will explain to anyone not in our field why this award bears his name.

One reason the Quigley Medal is such an honor is that the recipient is provided the opportunity to make a presentation to the members of AREUEA at the National Meeting, held in late May, normally in Washington DC.  This year, because of the coronavirus, the physical meeting had to be cancelled.  Fortunately, through the efforts of meeting chairs Lauren Lambie-Hanson, Mike Eriksen, and Karen Pence, and many others, AREUEA was able to use the teleconferencing facilities of the Philadelphia Federal Reserve Bank to hold a virtual meeting of some 400 AREUEA members, many of whom attended the Quigley Medal presentation.

When I began work on the presentation, I quickly determined that it would have a global focus.  While I've done plenty of work on U.S. urban development, especially housing markets and policy, it was doubtless my international work that prompted colleagues to seriously consider me for this award.  Plus, among many other accomplishments, John Quigley was one of the early driving forces in expanding AREUEA's horizons beyond the United States.

The next decision was about framing the substance of the talk.  For some time the following "eight big ideas" has served as a useful framework:

1. Stylized facts about urbanization and development around the world
2. Why cities exist; trade, economies of scale, agglomeration
3. Location within cities
4. Key assets: housing (the “real side”); housing finance; and commercial real estate
5. Transportation and other infrastructure
6. Local governance and finance
7. Environmental problems
8. The urban economy and the aggregate economy

In addition to the "big ideas," the Quigley presentation is an opportunity to make a few observations about how we work, who we work for, and where.

As I filled in the presentation, two further challenges presented themselves.  First, by the time March rolled around it was clear that I couldn't ignore the elephant in the room, the coronavirus pandemic.  That's been addressed in many other places, of course, but it had to be brought into the discussion.  Second, even superficial discussion of all eight "big ideas" would take us far over time for any such presentation.  So I chose to highlight just two, a look at some global patterns in urbanization and development; and location within cities.

The decision to use the phrase "time of stress" in the title instead of "pandemic" was deliberate.  The pandemic -- and possible future pandemics -- is not the only thing that could shock our economies and markets and societies in profound ways.  In other work, with Morris Davis and Julia Coronado on "The Future of Real Estate," we are examining a number of risks that could shock cities and our real estate markets.  Here's a "prescient" slide from our 2018 presentation to the AREUEA/AsRES conference in Songdo, Korea:

Slide from 2018 presentation to AREUEA/AsRES, Songdo Korea

Well, weren't we special -- we managed to squeeze "pandemics" in as the last bullet point on the slide, with little more to say about it.  Were we truly prescient?  Or were we the Jeane Dixons of real estate economics -- make enough predictions and maybe one of them will come to pass?

If the coronavirus was one elephant in the room, by the time of the presentation, there was another source of stress.  Three days before our virtual meeting, George Floyd was killed by a Minneapolis policeman, and by the time of the presentation, demonstrations against his killing, and against a wide range of racial disparities and injustices, had begun around the U.S. and, eventually, the world.

Racial (and ethnic) disparities, discrimination, segregation, and a host of related issues can be examined under each of the eight "big ideas."  My own modest work in this area has focused mainly on housing.  It could certainly be argued that the topic is worthy of separate recognition as a ninth "big idea."  And it fits into the global perspective of the presentation, since issues of race and ethnicity are hardly unique to the United States, although our history has produced some outcomes that are unique to this country.  The issues are large and require more than brief discussion.  Therefore I've abstracted from them in this presentation.  In due course I'll have more to say in this blog about some of them, separately.

The Links You've Been Waiting For

Shortly after I made the presentation, AREUEA posted a pdf version of the slides here.

The native PowerPoint contains notes attached to many (not all) of the slides, which will help decipher some of the material.  Download the PowerPoint slides here.  Anyone interested in using some of these slides in their own teaching or presentations can easily pull them out and add to your own PowerPoint.  Feel free to do so, though attribution is always appreciated.

Thanks to the good offices of the Philadelphia Fed, we have a video of the original presentation, which you can download here.  Haircut during a pandemic?  Not for me!  Also, you can see I'm a bit out of practice, since I insert an "uh" about every other sentence.  Students:  be warned, learn to excise these kinds of tics from your presentations.  Do as I say, not as I do!

More to Come

I will, in due course, add an expanded version that touches on all eight of the "big ideas.  Watch this space!

The Greatest Professional Honor I Will Ever Receive

Regular readers of my blog will know my admiration for the late John Quigley, longtime stalwart of Berkeley's economics department and real estate programs, esteemed scholar who I was proud to call a friend.

Shortly after John passed away, the leading academic association in our field, the American Real Estate and Urban Economics Association (AREUEA) decided to honor him by creating the John Quigley Medal.  You can read more about John at the appreciation I posted on the occasion of his untimely death in 2015. 

In January I received surprising and humbling news, at the San Diego ASSA meeting;  AREUEA chose me for this year's John Quigley medal.  

From the AREUEA website:

"The John M. Quigley Medal for Advancing Real Estate and Urban Economics is to be awarded to the individual who best represents the many ways in which John significantly advanced the academic fields that span his collective works. These fields include real estate, urban economics, public finance, regional science and others.

"The medal will recognize the many dimensions in which John contributed to these fields. Specifically, candidates for the John M. Quigley Medal may have produced a record of scholarship that opens up new avenues of inquiry, have a demonstrated record of mentorship of young scholars, have supported institutional advances within these fields, or have been particularly effective at dissemination of these fields to public and professional practices. 

I'm humbled to receive this award, first, because of my regard for John; second, because it's the highest award to which I could ever aspire, and from peers; and third, because of the list of terrific scholars who've preceded me in receiving this award.  Fourth, because it's not false modesty to mention that there are a number of outstanding scholars who are sure to join our list in due course.

Let me briefly introduce the other Quigley medal awardees, to date.

The first recipient of the Quigley Medal is somebody very familiar to regular readers of this blog.

Chip Case, Innovator in the Measurement of House Prices, and Teacher Extraordinaire

Karl "Chip" Case was Professor of Economics at Wellesley College, and, with his friend Bob Shiller, creator of the eponymous Case-Shiller repeat-sale housing price indexes. 

Like his good friend John Q, Chip passed away too young, in 2016.  Too soon, but not too soon to be awarded the first-ever Quigley medal, and deservedly so.

Sadly, a few years after I had written my appreciation of John, Chip Case passed away.  I wrote about Chip’s passing, and his research and teaching here. 

Even though I’m the brother of a funeral director and have posted memorials of a few other late colleagues, such as Art Goldberger and Austin Jaffe, I haven’t written about any other John Quigley medal winners. Not because they aren’t worthy – far from it. It’s because they are all, thankfully, still with us. 

So, having already written about John and Chip, let me pen brief notes about the other four scholars who proceeded me. I’m proud to know each of them, great researchers who have taught me much over the years.

Jan Brueckner, Versatile Leader in Urban Economics

Jan Brueckner of UC Irvine's Department of Economics has written many seminal works in urban economics and related fields.  He’s graced Irvine for a decade and a half, after two decades at the University of Illinois (Champaign-Urbana) as well as serving as a visiting scholar at dozens of other institutions over the years. 

For the better part of two decades Jan helped shaped our field as the editor of our leading journal, the Journal of Urban Economics. Jan’s own research spans of both theory and empirics. I have long admired Jan's approach to theory as he clearly follows Einstein’s dictum: “everything should be as simple as possible. But not more so.” The breadth of topics Jan has researched is extraordinary.  His research spans housing, commercial real estate, urban models, public finance, mortgage design, and transportation.  In the latter field he's especially known for his work on airlines. It’s hard to pick favorites from someone who hit the century mark in publications over 50 papers ago, but let me mention a few.

I’m intensely interested in global urbanization patterns. Since its publication in 1970, John Harris and Michael Todaro’s paper “Migration, Unemployment and Development: A Two Sector Analysis,” and its progeny, have influenced academic research on migration and the growth of cities. Their findings of also influenced many government policymakers’ view of the urbanization process in countries undergoing migration from rural areas to cities.


The essence of the Harris-Todaro model, is that urban workers in such countries – “less developed in the jargon of 1970” – are bifurcated into to separate markets.  One is a high wage formal sector in which wages are set artificially high by minimum-wage and other regulations and institutions. Most workers labor in the other market, an informal sector with a much lower productivity-determined wage. In the Harris-Todaro model, the possibility of obtaining a high wage in the formal sector job attracts migrants from the countryside. But there are more such migrants than such jobs in their model, because of the presumed large gap between formal and informal sector wages. So there is therefore “too much” migration to the city, a wage gap that is not bid away in equilibrium, and an increase in structural unemployment.

As my late friend Steve Mayo used to say, “it would be a good story if it were true.”  Research by Ian Scott; Jeff Williamson; and Sangeepta Pratap and Erwan Quintin represent many studies that show the assumption of strongly segmented labor markets with large wage gaps don’t hold up to empirical verification. 

Despite these results, Harris-Todaro remains a "zombie theory." Despite having been largely killed by number of empirical studies, it still walks the earth.  Professor Brueckner and his co-author Hyun-A Kim drive a new stake into its heart, with their paper "Land Markets in the Harris‐Todaro Model: A New Factor Equilibrating Rural‐Urban Migration."  As their paper's title suggests, they show that once you introduce a land market, even granting segmented labor markets, urban residents take steps to smooth their consumption between periods of employment and unemployment; any formal-informal wage differentials will be capitalized in land prices, throttling the excess migration to the city.

Professor Brueckner has also helped me in my teaching.  I taught urban economics since 1990, mainly to business students; most of those were real estate majors. Perhaps 20 percent of my urban econ students, on average, came from outside the business school. The majority of these were from urban planning; others came from public affairs and geography. Economics students were very welcome, whether from the economics department or from agriculture and applied economics.  These combined econ majors were often outstanding participants, but maybe 5 percent of my urban students over the long run.


Because I teach many more business students than economics students, I spend much less time on formal modeling than most teachers of the subject. Of course I expose my students to the basic models – supply and demand, the Alonso-Muth-Mills model, Tiebout equilibrium, regional models such as input-output analysis, and so on. 


Why spend less time on models than I would if I were teaching in econ department? The majority my students will, for better for worse, never write down a formal model, algebraic or geometric, once they leave my class. But they will work with data, constantly. So I spend a lot more time than is normal in such a class on data analysis and applied statistics. I sneak in a little bit of econometrics and I make sure they understand some basic properties of time series, ideas about endogeneity, and so on. I even try to talk them into being informal Bayesians. But for econ students and anyone else desiring a bit more of the traditional approach, I tell them to straightaway get a copy of Jan’s admirable little book, Lectures on Urban Economics, and work through the mostly geometric models in that volume. For advanced students I go a little further and recommend a work through Yannis Ioannides' excellent book From Neighborhoods to Nations: The Economics of Social Interactions.


If space permitted, we could delve into many other topics Jan has investigated, including the economics of airlines and other transport-related topics.  Other favorites, which I'll refer to in later posts, include his work with Alain Bertaud on the effects of development regulations ("floor space index" in Mumbai, or "floor area ratio" as it's known in the U.S.), and a terrific paper Jan wrote with Yves Zenou on how the canonical Alonso-Muth-Mills model (see below) can be fruitfully modified to incorporate location-specific amenities.  Jan and Yves use the model to explain why amenities-rich central Paris looks so very different from the downtowns of many U.S. cities. 

In addition to his contributions to urban and regional economics, Jan is also an accomplished photographer.

Don Haurin, A Housing Economist's Housing Economist

Don Haurin is Professor Emeritus of Economics at Ohio State University.  (Excuse my mistake, THE Ohio State University.  As a long-time Wisconsin prof, I sometimes forget myself.  Joking aside, whether you are in the Big 10 or not so lucky, even Badgers acknowledge that Ohio State has also married top scholarship to a great football program).

For many years I was skeptical of the proposition that it made much difference to household welfare whether someone owned or rented their home, beyond certain subsidies homeowners received through the tax code, and built into U.S. financial policy (Fannie Mae, Freddie Mac, FHA, etc. See Green and Malpezzi for a summary of these subsidies.  The Tax Cut & Jobs Act 2017 has greatly reduced, though not completely eliminated, tax preferences for homeownership.)  

The tail wags the dog I thought, since these subsidies are usually justified by the purported existence of other, more general social benefits to homeownership.  See for example, arguments from the National Association of Realtors, or Freddie Mac.  Note that these links are recent, but the interest in homeownership goes back many decades; see, for example a statement on the subject from Herbert Hoover.


So about 25 years ago my friend Richard Green, who then shared my skepticism of wide-ranging homeownership external benefits, teamed with Michelle White to investigate whether there was any support in the data for such benefits to homeownership. Of course, correlation is not causality, and homeowners are very different than renters. To begin with everyone knows that American homeowners on average have significantly higher incomes than renters; they are also more likely to be white, even after controlling for income differences. We could list a number of other demographic and financial differences that could make it hard to cleanly estimate the effects of homeownership per se on household outcomes and characteristics. Thus the first problem Green and White had to tackle was this possibility of a spurious correlation between social outcomes and homeownership. 

To keep our discussion here brief, suffice it to say that Green and White examined several data sets and controlled for a number of other independent variables that could have driven social outcomes. Social outcomes could include, for example, how long children stayed in school, whether they later had contact with the criminal justice system, and so on. They also used  econometric methods (described in more detail in their paper) to mitigate problems from real-world imperfect specifications. To their surprise they found that homeownership mattered even after controlling for as many other determinants as they could muster and adopting the best available methods for sample selection bias.

Green and White’s paper touched off a flurry of additional research on this topic.  In a series of papers Don Haurin and co-authors have greatly extended and enriched this literature. A good example is Dietz and Haurin’s (2003) survey paper, in which they examine homeownership’s possible effects on:

• Household wealth and portfolio choice
• Household mobility
• Labor force participation
• Urban structure and segregation
• Home maintenance
• Political and social participation
• Health
• Demographic outcomes 
• Self-esteem
• Child outcomes

Dietz and Haurin provide a critique of the many studies that lack sufficient control variables, both variables that affect the outcome of interest (e.g. child outcomes) and the key intervening variable of homeownership. In addition to garden-variety omitted variable bias, we may be confounding the effect of the homeownership rate with the missing variable, so that observed results may be spurious.

These and other econometric problems are reviewed in Dietz and Haurin. They also examine how choice of model and the type of data can mitigate these problems. Best practices are described.

Their critical review of data, and econometric methods, suggest pre-1990 findings can be suspect. More recent studies with better data and methods, like Green and White, attacked the endemic sample selection problem full on, although it’s only mitigated, never completely defeated. The evidence reviewed by Dietz and Haurin support the idea that the desire for eventual homeownership is an important motive for increased household savings. Other mooted effects on labor supply, fertility and consumption are “not proven.” 

Homeownership lowers mobility, as is well established, but potential spillovers into labor markets are harder to find. Positive ceteris paribus effects of homeownership on child outcomes such as education and lack of contact with the criminal justice system have been repeatedly documented. Many knowledge gaps remain, including but not limited to analysis by type of structures, and differential impacts by race and ethnicity. Despite a number of research papers, evidence on the relationship between housing tenure and support for restrictive regulations and housing market – William Fischel’s famous Homevoter Hypothesis – is still somewhat fragmentary. Much remains to be done.

Some of these findings are commonly accepted, notably that homeowners move much less often than renters, and that homeowners often have very unbalanced wealth portfolios. For  many Americans, particularly the middle-class, the equity in a single house is their main financial asset.  This opens the middle class up to risk, since those prices are correlated with the health of the same market (metropolitan area) in which they sell their labor.  Of course, renting may reduce exposure to this risk, but in fact low- and moderate-income renters invest less in other non-housing financial assets, too, as Grinstein-Weiss et al. (2013) demonstrate. 


Other often cited, superficially plausible in the data, have not been carefully tested for example links between homeownership and political participation, urban form, or support for restrictive zoning. Solid studies find that homeownership affects a number of child outcomes such as school completion rates.

Further, Dietz and Haurin point out that the majority of the studies they review frame possible outcomes as positive effects of homeownership. Studies of stresses on households who are underwater and/or in default on their mortgages are one example of negative outcomes that remain fertile fields for future research.


Prof. Haurin has studied a wide range of other topics on housing and urban issues. He and his co-authors, often former students, have undertaken studies of fluctuations in the rate of homeownership; how to construct improved real estate price indexes, and use them to study house price volatility. He’s undertaken some of the earliest research on Hispanic experiences in housing markets, as well as more technical work on the prediction of turning points in house prices. More recent research has focused on rigorous modeling of how sellers set list prices for houses, and how expectations are best modeled in the housing market.

Jim Kau, Who Changed the Way We Look at Mortgage Defaults

James Kau, C. Herman and Mary Virginia Terry Professor Emeritus of Business Administration at the University of Georgia, has made many contributions to urban economics; housing; but he's best known for his work in real estate finance, especially rigorous models of mortgage default and prepayment.

Professor Kau’s research interests are especially broad, spanning public finance and governance as well as real estate and finance.  In my world he’s best known for his work on mortgage analytics. 

A good place to start is his 1995 survey of “Option Theoretic Pricing of Mortgages,” with Donald Keenan. Mortgages of all kinds, like most loans, are subject to default and subsequent losses. The majority of U.S. residential mortgages are made at fixed rates, but are prepayable without significant penalty. Thus, when rents fall, rational borrowers prepay, and refinance at the new lower rate. Since the prior lender loses a formerly profitable cash flow stream and must now reinvest in a less favorable rate, this prepayment risk is of considerable interest, along with more common default risks. Furthermore, these twin risks are not necessarily independent. Falling rates presumably increase the probability of prepaying; the new lower rate after refinance may forestall some defaults, to give but one example.

Most U.S. mortgages are nonrecourse, which reduces but does not eliminate the cost of rational default, i.e. default when the value of the mortgages exceeds the value of the house.

It’s conventionally accepted, and perfectly logical, that falling rates can raise housing values and thus make defaults less likely.  (See evidence here that it’s capital flows, more than rates, that drive prices; but there’s ample evidence that rates still matter.)

So, what’s special about the option theoretic approach to mortgage termination? Consider an example of prepayment. A borrower currently has a mortgage, and is contemplating prepaying the old mortgage and taking out a new loan for the existing balance.  Supposed to keep it very simple we assume the transaction costs of prepaying and taking out of new mortgage (points, fees, the value of time spent) is some fixed amount; say $500. Suppose the borrower knows that they will move in 10 years, terminating the mortgage at that time. Both old and new mortgages are standard fixed-rate self-amortizing designs. 

Suppose that rates are falling. In one model of default, based on a “razor’s edge” decision, the borrower computes the present value of 10 years of payments at the old and new rates; and as soon as the present value of the savings exceeds $500, they refinance. But if they refinance immediately, they give up the option to refinance later with still lower rates. Of course rates could rise instead of fall, which makes the modeling a little interesting.  

Nevertheless, any time an agent takes a decision that commits them to a course of action for a period of time, an option is involved. Dixit and Pindyk is an excellent introduction to the structure of several kinds of options and how to price them.

Kau, Keenan and Munneke analyze mortgages as a type of option on an underlying asset, namely a house. Many analysts faced with options turn immediately to the workhorse Black-Scholes option pricing model for a simple stylized asset, specifically the short run call option on a security with a fixed return. Kau and colleagues point out that mortgage contracts are, of course, more complex. 

Specifically, Kau et al. survey how to set up models that can be used to price several alternative mortgage designs. Mortgages can be characterized by fixed payments or adjustable rates with experiments or graduated payments. They do not limit themselves to housing markets.  We have already noted that residential mortgages in the U.S. are generally nonrecourse with inexpensive prepayment.  Commercial mortgages generally have recourse and pre-payment penalties and lockout provisions that require a different model. The black Scholes model is of little direct utility here.  Kau et al. review and explain solution methods for applicable models, using both continuous and discrete time variants.

The Kau and Keenan paper is one of several places to begin the rigorous study of mortgages. See Green (2013) and Fabozzi (2016) for example. As Kau and Keenan note there many extensions possible and sometimes required, for example how the state variables of interest rates and house prices are modeled, and introducing explicit transaction costs into the models. And of course of mortgages are derivative securities written on real estate assets the events of the 2000’s remind us that we began to commonly write derivatives on derivatives – and sometimes derivatives on the second derivatives – which could get us into some wicked hard problems when trying to evaluate and price these instruments. Kau and Kennan provide a good entry point to those problems.

A particularly interesting 2011 paper by Kau Keenan and Munneke investigates an oft-debated issue, “Racial Discrimination and Mortgage Lending.” This is a complex and fraught topic; the Green and Malpezzi Primer on U.S. Housing Markets and Policy provides a nontechnical if dated review.  As noted above, an important strand of Jim Kau’s research involves rigorous simulation modeling of both major types of mortgage termination, namely prepayment and default. In this paper Kau and co-authors bring their analytic technology to bear on testing for racial discrimination in the mortgage process.

Unconditionally, minorities have higher rates of default and foreclosures (see e.g. Gerardi and Willen, Subprime Mortgages Foreclosures and Urban Neighborhoods). By itself this factoid tells us little. Minorities (most studies have focused on black-white differences) tend to have lower and more volatile incomes, (Ziliak, Hardy and Bollinger), lower credit scores, and perhaps a dozen other systematic differences that confound the relationship between race and mortgage default.

One understudied effect comes from racial differences in prepayment behavior. Black borrowers are less likely to prepay when rates decline, as Kau et al. document.  Ceteris paribus, such behavior should make black borrowers more attractive to lenders. There are several ways lenders might treat attractive (or unattractive) borrowers differently. The first of course is whether they lend at all. A second is whether loans are offered on the same terms – including but not limited to loan-to-value ratios, and of course interest rates. The latter is the focus of Kau et al.

Using the framework developed by Deng, Quigley and Van Order, Professor Kau and colleagues found that once they controlled for prepayment, as well as loan-to-value and other contract terms, borrowers in predominantly black neighborhoods significantly were offered higher contract rates than the full model predicts for color-blind competitive markets.

Professor Kau’s study is of course not the last word. Among other issues, as laid out clearly in the review paper by Anthony Yezer, a clear understanding and clean estimates of true mortgage discrimination require studying the entire process, from application through mortgage termination. Data requirements are hard to meet, and such studies have not yet been done.

Many questions remain. Kau et al. did not have credit score data, to point out one shortcoming; and neighborhood variables are useful but imperfect proxies for household circumstances.  Taking the results at face value, competing hypotheses remain for why such differentials might exist, as Kau et al. note. Is it straight racism? Unconscious bias? Ignorance of the true model of prepayment and default? The authors suggest another logical possibility, a potentially perverse explanation that in making loans truly race-blind lenders could be failing to credit minority borrowers with the benefits of their lower propensities to prepay. Whenever the underlying cause, Professor Kau and his colleagues provide an interesting twist on the standard stories of and debates about racial discrimination in mortgage behavior.

Ed Mills, One of the Founders of Modern Urban Economics

Edwin Mills is Professor Emeritus of Real Estate at Northwestern.  Before his tenure at Northwestern, he was a long-time Professor of Economics at Princeton.  Mills is the author of some 20 books and 130 papers.  For many years he was the sole editor of the Journal of Urban Economics, the leading journal of our field; and he has served as co-editor and editorial board member of many other journals.

My simple introduction to him in class: the father of urban economics.  I think the other Medal recipients would point to Professor Mills' deep influence on all of us.  Even John Quigley would give Ed pride of place, along with John's own mentor, the late John Kain; William Alonso; and Richard Muth.  We often refer to the canonical model of land use within a monocentric city as the "Alonso-Muth-Mills model" or simply "AMM."

Ed was the third recipient of the Quigley medal. His career is so distinguished that we are not dissing Chip or Jan in the least if we ask, “what took us so long?” (In fairness, there are a couple people coming after me who might ask the same question.) One measure of the esteem in which Ed is held is that for two decades AREUEA has awarded the Edwin Mills Best Paper Award for the top research presented at our annual meeting.

My first textbook, as a student, and again as a teacher, was Edwin Mills and Bruce Hamilton’s Urban Economics. (Bruce Hamilton, one of a legion of outstanding Mills PhD students over the years, served as co-author in later editions.)  Now out of print, Mills and Hamilton has been largely superseded by Jan Brueckner’s fine introduction to the theory (see above), Arthur O’Sullivan’s wide-ranging undergraduate textbook, John McDonald and Dan McMillen’s excellent foray tying urban economics and real estate together, and a classic if somewhat older text on real estate and urban from Denise DiPasquale and Bill Wheaton. (Denise and Bill are currently updating their fine text, so watch that space). While each of these fine textbooks has extended Mills’s original textbook, I think these authors will all acknowledge a debt to Ed Mills and his co-author Hamilton. With 1600 citations in Google scholar, it’s long been viewed as a standard.  

I eventually migrated from Mills and Hamilton to other texts, but I long continued assigning his beautiful review of the field published by the Brookings Institution.

Along with Alonso’s Location and Land Use, and Muth’s Cities and Housing, Edwin Mills’ Studies in the Structure of the Urban Economy was one of the foundational works in urban economics, and along with some classic “second generation” works like Kain and Quigley’s Housing Markets and Racial Discrimination formed the basis of my own early education in the field. 

A follow-on paper by Mills and Tan on population density set the stage for my own work on international comparisons with Alain Bertaud. Mills and Tan relate flattening population density gradients to rising incomes and growing cities.  Again Mills used simple data, the best available, and they were careful to make mainly qualitative comparisons.  In our study, thanks to Alain’s careful work over several decades, we have the advantage of comparable data collected and analyzed for over 50 large metropolitan areas around the world.

My colleague and good friend Kyung-Hwan Kim was one of dozens of Mills PhD students; as I’ve remarked elsewhere, Mills work with Kim and other co-authors was instrumental in opening my eyes to the economies of Seoul and other fascinating Korean cities.

Ed is one of several scholars whose interest in land use and development regulation inspired my own -- Bill Fischel and Lou Rose also come to mind, among others, and of course I built some of my domestic U.S. work on data collection by Peter Linneman and Anita Summers.  My international work in this area was inspired by Alain Bertaud and Ramgopal Agarwala, among others.

I was especially taken with Mills’ simple but elegant analysis of urban population density gradients in Studies in the Structure.  The monograph applied simple but clever techniques to the limited set of historical data then available.  Mills’ results provided a number of insights that were later confirmed as we developed more detailed small area data and better technology for analysis (geographical information systems, advances in spatial econometrics). 

One of my first papers with Jim Follain tested some of Mills’s predictions.  I often paired Follain and Malpezzi with a related paper by Mills and Price to teach students how simple, not terribly realistic urban models could nevertheless tell us a lot about urban decentralization (later referred to as “sprawl”). By comparing the two papers I could also explain to students how some results (for example the effects of income on decentralization) could be robust to different studies and methods; while others (is race or poverty a stronger driver of decentralization?) are more fragile than we’d like. Our classroom review highlighted how you can learn a lot from empirical work; but how you should also never put too much stock in any single study, whatever its care and/or prominence. 

This paper jogs my memory of an early formative experience. Follain and I had recently published our paper on tests of the standard urban model of Mills and others, when I then moved from the Urban Institute to the World Bank. In grad school at the same time, I’d recently shifted from international affairs to economics.  I hadn’t yet taken my field exams, much less finished my Ph.D. Doug Keare, my new division chief, invited me, as the literal new kid on the block, to present my paper with Follain in a lunchtime seminar. Okay. 

Entering the room, I found my new friend and colleague Steve Mayo, and a number of accomplished World Bank urban economists including Keare, Kyu Sik Lee, Andy Hamer, Greg Ingram and others.  Also in the room were several visiting scholars: Vern Henderson, Jeff Williamson, Charlie Becker, and Ed Mills. I gulped because that’s quite a lineup and I was still a lowly graduate student. I felt pretty  overmatched. 

Nevertheless, I proceeded to present my paper, arguing at one point Greg Ingram’s sophisticated simulation models with John Kain and others hadn’t fully superseded the simpler monocentric model. Before long I was explaining how, in our view, the Mills model nevertheless needed to incorporate a number of variables that could be described as – gasp – sociological.

I awaited my shredding. It never exactly came. I think Ed and the rest must’ve recently all read Portia’s famous speech about “the quality of mercy.”   
They gave mercy and I took it, with gratitude. There was a spirited discussion, and plenty of healthy criticism of my work. But I learned the value of, in today’s jargon, disagreeing without being disagreeable. There was never a hint of anyone’s rank or reputation, just the quality of data and method an argument, and a collegial search for truth, or as close to truth is we could come. I was presenting, and they did me the honor of treating me as an equal for the duration, though I was anything but.


Over the years, Ed Mills, and the others in that room, and several others taught me a shortcut to evaluating someone’s character. How do they treat those below them in the food chain? With respect, evaluating their ideas and work on the merits, without condescension? How do they treat those nominally above them? Evenhandedly, again appropriately respectful, but exhibiting the integrity and sense of self and yes occasional courage to offer their honest criticisms, without regard to their own popularity or consequence? This integrity is something I saw early and deeply in Ed Mills, in John Quigley for whom this medal is named, and I’m pleased to say in each of my preceding award winners.

Nobody Gets a John Quigley Model Without a Lot of Help

The secrets of my success?  As Hall of Fame pitcher Lefty Gomez would put it, "Is it better to be lucky or to be good?"  My usual answer is "yes," though along with Robert H. Frank and Benjamin Franklin, I put a lot of weight on luck. I come from a family that spent the last century or so moving from tenant farmers in Italy, to coal miners in Western Pennsylvania, to small scale (and small profits) tile and terrazzo installation. Like Warren Buffett, I "won the ovarian lottery;" it's still an advantage, ex ante, but in 1952 to be born a white male in the United States was a huge leg up.  I was one of about 1.7 million 1952 claimants of that particular prize in the lottery, out of about 98 million global births; roughly 1 out of 60.  And with the help of family, good teachers, and some more luck, I was well educated.  To my knowledge I am the second in my family to finish a BA, and the first graduate degree, although many others have followed. 

I've continued the string of good luck, especially  when it comes to colleagues and co-authors.  If I've had one talent over the years, it's been to find great people to work with.

Here's a photo of me with GWU Economics Professor Tony Yezer, who was my PhD advisor.  Tony was and is a great teacher, and has been a great supporter in many other ways over the years.

From the Urban Institute to the World Bank to the University of Wisconsin, I've had at least 40 coauthors and hundreds of other supportive colleagues, from whom I've learned a lot.  And that doesn't even begin to count literally hundreds of others I could name over the past 40 years, AREUEA members and many others, from dozens of countries.  Obviously I can't do justice to all those colleagues and friends here, but in addition to Tony, I have to mention a few by name here. 

Of many colleagues at the Urban Institute -- Ray Struyk, who got me started, Larry Ozanne and Tom Thibodeau, a dozen more come to mind -- I have to give my friend Jim Follain credit for giving me the opportunity to move from working as an assistant to becoming a full partner in research and to generously sharing credit for our joint work.  

At the World Bank, I learned so much from colleagues like Bertrand Renaud, Jim Wright, Bob Buckley, Larry Hannah, and of course Alain Bertaud; again far too many to name.  And the Bank gave me to learn from dozens of colleagues, Vinod Tewari, Duncan Maclennan, Marja Hoek-Smit, Solly Angel, Kyung-Hwan Kim are the tip of that iceberg. But one person stands out among that large list, my late friend and colleague Steve Mayo.  When the late Doug Keare and others organized a research project on "Housing Demand and Finance in Developing Countries," they hired Steve to direct the project, and me as the directee.  But again, like Jim Follain, Steve was quick to share responsibilities and credit, and to mentor me through a critical stage in my career.

Plus, it was through Steve that I came to know John Quigley as a friend as well as a remarkable source of insight and knowledge.

When he hired me, Doug suggested it would be a good place to work for a couple of years while I did my graduate work at GWU with Tony.  But a couple of years passed; I finally finished my PhD; but I forgot to leave and ended up staying for nine years.  Later, as I came up on a decade of "service to the Bank," as the phrase is tossed around there, I began to look elsewhere for the next stage of my career.  Kerry Vandell had recently moved to Wisconsin to rebuild the real estate program after the untimely death of master teacher James Graaskamp.  Kerry's first personnel move was a great one, to hire Jim Shilling.  With Kerry and Jim in place, I knew it was a place I'd like to be, so I jumped at the chance when they offered me the opportunity to join them.

My 26 years at Wisconsin were a wonderful opportunity, with many colleagues both within the Graaskamp Center as well as elsewhere in the Wisconsin School of Business and throughout the University.  I could quickly name a hundred. Wisconsin was also a point of entry for a wide range of real estate and urban development professionals, some of them UW alums, some Graaskamp Center Board members, far too many to name here. And don't forget the 3,000 students I taught there over the years.  More than a few of those students taught me back, to be sure.

But of all those marvelous Wisconsin connections I'd have to give pride of place to my friend, colleague and co-author Richard Green.  Richard joined up in 1990, the same year as Jim Shilling and myself.  Richard had recently completed his Ph.D. in economics, writing on international trade under famed UW Professor Robert Baldwin.  While Richard's wife, Patricia Harris, completed her medical education at UW, Richard took a temporary job as the Chief Economist of the Wisconsin Realtors Association.  I might go even further, but no one could question that no other state real estate trade group has ever had such an accomplished economist grace their staff. Together Richard and I wrote half a dozen papers and our Primer on U.S. Housing Markets and Housing Policy; we could have, should have, written a dozen more.  (Though we're not done, yet.)

I blog, but I don't do Facebook or other places where you'll find photo galleries.  So here are a few photos of colleagues whom I've learned from and collaborated with over the years; these are primarily friends I've also coauthored with at one time or another.  If you want to see more, I've been collecting more photos within the presentation I gave a few years ago when I retired from Wisconsin, click here for the current version.  If you're a glutton for punishment, and want to read more of those papers, you can find a (slightly dated) CV here, and my entries at Google Scholar here.  But of course now that I'm blogging, well, check out my other blog entries here.

I'm Not Quite Done Yet...

As an Emeritus Professor, I remain connected to the Graaskamp Center and to the University of Wisconsin more generally.  I've long been connected to the Homer Hoyt Institute, and for the last several years I've been honored to serve as the Dean of the Hoyt Group's Weimer School Academic Fellows.  Morris Davis, formerly a colleague at UW, and now Paul V. Profeta Professor of Real Estate at Rutgers, has recruited me to help out with their Center from time to time.  

And it's great to live in the Boston area.  We moved here in 2014 after retirement for the temperate climate.  On arrival, we found one of our grandsons lived here, what a coincidence! With his parents, too, that's OK.  Another great coincidence is that it turns out there are several universities in the area, and many friends at MIT, Harvard, Boston College, Tufts, and the Boston Fed have been very welcoming and generous with their time.  And of course I remain an active member of AREUEA.


I'm working on several projects at the moment, some with long-time coauthors (see above), some with some new ones.  Yongping Liang and I are undertaking some new research on the metro-level analysis of housing supply and pricing.  Jaime Luque, Antonio Mello, Stani Milcheva and I are writing a monograph on housing affordability, building on work we did for Stani's conference on the topic at UCL two years ago.  Kyung-Hwan Kim and Peter Englund and I are revising a long-dormant paper on tenure choice across countries.  Jay Sa-Aadu and I are revisiting urban policies in Africa, with our colleague Moussa Diop.  And someday Richard Green and I will finally finish the second edition of the Housing Primer, with some help from Paul Carrillo.

Final Thoughts

The Quigley Medal is a very special honor both because it's from an Association I've learned much from and made many friends in; but also because  it is named for John.  You might have seen what I wrote about him several years ago, here.

As a further honor I'll be asked to deliver an address to AREUEA when we meet online again in DC at the end of May.   (Yikes!  That's next week!)  Another blog post, coming soon, will tell you more about that address and provide some links for interested readers.

UPDATE:  You can now find that presentation -- in PowerPoint, pdf, and a YouTube video -- here.

In the meantime, you can find out more about the Virtual National AREUEA Conference here.  If you register by the end of May 27, you'll receive an email with instructions on how to sign into the conference along with links to papers and slides.

The AREUEA webpage notes that the conference is free to AREUEA members, but as I understand it, the conference is actually free to anyone who registers.

Here's the Conference Schedule:

May 28

11:30-11:35	Welcome and Logistics
11:35-1:15	Commercial Real Estate (4-Paper Session)
1:30-2:15	Quigley Medal Lecture (Steve Malpezzi)
2:35-4:15	Urban Economics (4-Paper Session)
4:30-5:15	WREN Panel "Demystifying the Journal Publication Process"
5:15-6:00	WREN Networking Event

May 29

10:00-11:40	Household Finance (4-Paper Session)
12:00-1:40	Housing (4-Paper Session)
1:40-3:00	Industry Panel on Housing and Mortgage Markets

 I hope to "see" you at the conference on Thursday!

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