Housing Supply and Demand: Some Basics

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

Interesting digression: historian 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

Exhibit 1

Exhibit 1: Some of Wisconsin's rural curves.

"Curves" can also be straight, as we'll sometimes draw them below.  As Wikipedia reminds us, "a curve is the image of a continuous function from an interval to a topological space."  Less formally, a curve is a generalization of the concept of a line.

Exhibit 2

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 here, as well.

By convention, we often hold most of the determinants of demand and supply fixed (ceteris paribus assumption), and graph a curve representing how demand and supply vary with a single determinant, viz. the price of the good.

Changes in other variables (income, financing, price of inputs, etc.) are handled by shifting the curves. Exhibit 1 illustrates, presenting market-level supply and demand curves.

Exhibit 3

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 in Exhibit 3, 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 4, 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.

Exhibit 4

Supply and demand representations like Exhibits 3 and 4 present “demand in price-quantity space,” focusing on price as the determinant of demand while holding other demand and supply shifters (determinants) constant.

We will also often draw demand curves in “income-quantity space,” holding price (and other demand shifters like demographics) constant. We call demand curves that focus on income instead of price Engel curves, after Ernst Engel; these will be discussed in a companion posting, coming soon.

Downward sloping demand curves (in P-Q space) are perfectly intuitive in most situations. They can be derived informally by writing down actual, or plausible, “demand schedules” for the good in a simple table, as can be found in any introductory text such as Case, Fair and Oster; or in Samuelson and Nordhaus. They can be derived more rigorously from a representative consumer who maximizes utility dependent on the consumption of housing and other goods, subject to a budget constraint, as is worked out in any intermediate text such as Nicholson, or Watson and Getz.

Why do demand curves slope down? In any market there will be a few consumers who really want a new house, and would be willing to pay a lot for it. But as we consider more and more consumers entering the market, the price has to fall to draw them in.

Similarly, upward sloping supply curves are perfectly intuitive.  They can be derived informally using tabular supply schedules, as in the principles texts, or as the result of a supplier’s (developers, landlords) profit maximization subject to the technology embedded in a production function, worked out in the intermediate microeconomics texts.

A supply curve is a marginal cost curve. It is positively sloped in the short run (more supply => higher price per unit).  As an industry expands, this drives up price of scarce units (like oil and labor).

Why do supply curves slope upward? The principle reason is diminishing returns. If the city’s developers want to produce more housing, they will start to bid up the price of suitable parcels; best parcels (e.g. requiring little bulldozing), on offer from willing sellers, will be used first.  But to bring more parcels into the market may require a higher price, to cover the costs of fixing topography and soil, and convincing less willing landowners to sell.  Skilled labor will be increasingly hard to find, so construction wages would rise. These and similar processes would cause costs, the underpinning of supply curves, to rise as the quantity supplied rose.

Supply and Demand for an Individual Consumer

For the case of an individual consumer, abstracting from billionaires (AIG reports that their “ultra-high net worth clients” own an average of nine homes), we’re usually thinking of somebody consuming a single house.  Exhibit 5 presents such a diagram.

Exhibit 5

Now we want to measure the quantity of housing in a representative individual house, instead of counting houses.  We can do so using something as simple as square footage, or more complex like the quantity of housing services computed using multivariate hedonic indexes (see Green and Malpezzi, or Malpezzi). Let’s go with square footage, i.e. let’s assume our individual consumer is choosing the size of a single, otherwise standard house, and that the law of one price prevails in this market. So our quantity measure on the horizontal axis is square feet.  We will assume for simplicity that for our otherwise standardized house, our flow price measure is the rent per square foot.

The foregoing rationale for downward sloping demand curves, diminishing marginal utility, can be applied to both market level and  consumer’s individual demand curves. Some people have a stronger preference or demand for housing and others, and a market demand curve basically lines them up by how much they want a house, and reports their willingness to pay in order of that strength.

It’s reasonable to assume diminishing marginal utility of additional square footage. An extra foot, or 10, matters more when considering a 1000 ft.² unit, compared to a 2000 ft.² unit. Hence the demand curve in Exhibit 5 slopes downward. However the supply curve for an individual house is determined by the market price per unit of housing services (here square footage) and so the supply curve faced by our individual consumer is flat.  That is, we assume that our non-billionaire is one of many consumers in a competitive market, i.e. is a price taker.

In addition to diminishing marginal utility, we can also appeal to what are called income and substitution effects as reasons individual demand curves slope down. These are usually explained rigorously using indifference curves; you can find an explanation in any principles text.

The substitution effect is immediately intuitive: as the price falls and housing expenditure relative to other goods falls, we want to consume more of it. And vice versa, if the price rises we will consume less.

The income effect requires a little more thought. Given a fixed money income, in current dollars, if the price of a good falls (especially a good with a large budget share, like housing) real income, i.e. the overall purchasing power of their fixed current income, will rise. They will purchase more housing; and, under reasonable assumptions about the nature of their preferences, they will also consume more of some other goods, too.

A Few Unusual Cases

A demand curve is a marginal benefit curve. It is usually negatively sloped (higher price => lower demand).  There can be exceptions, e.g. "conspicuous consumption" of goods where quality is judged by price.  Such situations are often associated with Thorstein Veblen, analyzed more rigorously by Tibor Scitovsky.

Another example, much discussed but hard to find, is a so-called Giffen good.  A Giffen good is a good which is a necessity that takes up a large share of the consumer's budget; so large that if the price rises, spending rises so fast that it crowds out other spending.  The income effect -- declining real income from the price increase -- completely overwhelms the substitution effect. The classic example -- possibly apocryphal -- is potatoes during the Irish famine.  It's not clear, by the way, that this actually happened.  Another possible example, analyzed by Jensen and Miller, is the case of rice for very poor households in rural China.

Supply Meets Demand: Equilibrium

“Equilibrium: a condition in which all acting influences are canceled by others, resulting in a stable, balanced, or unchanging system.”

In our simple static model, equilibrium is the intersection of demand and supply curves.  At this point, an extra unit costs more than the market is willing to pay for that extra unit.  Prior to that point, an extra unit sells for more than it costs, so there is an incentive to produce it.

Notice that we usually assume that once in equilibrium (discussed below) the price of all these equivalent houses is fixed at the price paid for the final marginal consumer and supplied by the last marginal developer. Cases where this “law of one price” do not hold include cases of so-called “price discrimination;” the price of seats on a plane is the classic example. Also note that if the law of one price does hold, as in Exhibits 3 and 4, the first few consumers pay much less than their willingness to pay. This gives rise to consumer surplus, about which more later.

Similarly, in our example of a single consumer, Exhibit 5, our resident sets her consumption (square footage, or other measure of the quantity of housing services) by equating her marginal benefit to the market price of the last square foot consumed. But she gets all the earlier square feet at the same price (rent per square foot in this example). Those first square feet she pays much less in her true willingness to pay, giving rise to consumer surplus.

Equilibrium does not necessarily require any special knowledge, but can be reached through trial and error. Produce too much, and supply is greater than demand; units are left unsold, and the price falls, reducing the number of units supplied. Produce too little, and consumers with high marginal benefit, i.e. consumers to the left of the equilibrium point, will bid up the price, calling forth more supply.

Economists discussing equilibrium sometimes resort to a metaphor called the “Walrasian  auctioneer”. This metaphor is named after, wait for it an economist named Walras.  The Walrasian auctioneer is a fictional market-maker who coordinates consumers’ bids and suppliers’ asks. While some markets have actual auctions, many more behave as if there were such an agent. The Walrasian auctioneer can be a convenient fiction that mirrors actual market behavior.

By the way, we usually think of quantities demanded and supplied as the "dependent variable," and prices, along with incomes, costs, tastes etc. and "independent variables."  Let's note two points in passing that will come up later.  First, it's possible, and sometimes better, to consider price as the dependent variable.  If we make that assumption we are working with inverse demand and supply curves.  Second, and more to the point, sometimes both quantities and prices are "endogenous," i.e. are determined within our model.  But for now we'll simply note these issues, and move on.

Oh, before we move on -- you may have noticed that in the usual simple interpretation, where QD=f(P) and QS=g(P), we usually plot the dependent variable on the horizontal axis, and the independent variable on the vertical axis.  Weird! Because when you plotted your first equations in high school, you probably wrote y=f(x) and put the dependent variable on the vertical axis.  Why are economists backward?  Alfred Marshall!  When he wrote his influential Principles of Economics about a century and a half ago, he plotted Q on the horizontal axis and P on the vertical, and economists have done it that way ever since.

Partial and General Equilibrium

Here’s another useful concept, and its associated jargon. When we examine a single market in isolation, we call that partial equilibrium. But in reality, multiple markets are often interconnected. For example the flow market for housing (Exhibits 3 or 4) is also connected to the market for the entire housing stock. When we analyze two or more related markets together, economists refer to “general equilibrium.”

A good example of a simple general equilibrium model that the considers the connections between stocks and flows would be DiPasquale and Wheaton’s four quadrant model of real estate markets, which we will examine later in the course. Another approach is to examine spillovers between one market (city?) and another. This type of general equilibrium analysis is discussed in Pollakowski and Ray, to give one of many possible examples.

Comparative Statics vs Dynamics

Suppose we have a shock that shifts a demand (or a supply) curve.  What’s our new equilibrium, how do pre and post shock P's and Q's compare?  The above is a comparative statics question.  We compare two static (unchanging, but for the shock) equilibria.

Our comparative static models, above, do not tell us how long it takes to go from the first state to the second state; nor how, exactly, we get there, i.e. the path of adjustment.  Dynamic models try to tell us about adjustment paths; time is introduced explicitly.  In this posting we consider comparative statics only; for an example of simple dynamic models see, for example, Goodman (1995) or Malpezzi and Wachter.

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… virtually all the determinants discussed briefly around Exhibit 2 and our introduction.

Very roughly, steeper curves tend to be less elastic, and flatter curves more so.  But do not confuse the slope of the curve with its elasticity per se. The slope of the demand or supply curve is the absolute change in the quantity given price (or vice versa) if you prefer. The elasticity is the percentage change. Thus a linear supply or demand curve actually exhibits varying elasticity, since that constant slope has to be adjusted by a varying level at every point along the curve. A little experimentation will confirm, for example, that the demand elasticity of a straight line demand curve falls as we move from left to right.

A moment’s inspection also shows that in the special case of a straight-line vertical curve or horizontal curve, the elasticity is constant, at zero (perfectly inelastic) or infinity (perfectly elastic).

There are two polar cases where straight line curves do exhibit constant elasticity. Those polar cases are when curves (most often supply curves) are either vertical or horizontal. Then the curve is either perfectly inelastic or perfectly elastic. Market supply curves will often be assumed to be perfectly inelastic in a very short run; they may be perfectly elastic in a very long run.  Consider the case were supply and demand curves are drawn for representative agents instead of the market as a whole. Then the supply faced by an individual consumer in a competitive market will be flat, i.e. perfectly elastic.  The individual consumer can consume as much as they like without affecting the price.

Randall Munroe, the physicist and genius cartoonist behind xkcd, summarizes the polar cases of elasticity nicely:

Exhibit 6

Exhibit 7

Exhibit 7 presents an example of a demand curve which does exhibit constant elasticity. I’ve built a little spreadsheet model that allows you to insert different price elasticities of demand and supply to see how the shape of constant elasticity curves changes; you can obtain it by clicking here.

Thresholds: does supply respond symmetrically in both directions?

Sometimes supply and/or demand curves exhibit threshold effects.  There may be some minimum consumption required at (almost?) any price or income level; “basic needs” like housing and food may be modeled this way.  Mayo provides an example, using the so-called "Stone-Geary" model.  Another threshold may appear when you consume a lot; at some point consuming more chocolate cake or beer will make you sick (especially if you consume them together).  There may be some market threshold where government freezes production and consumption, as when there is a limit on the number of building permits issued.  O'Sullivan is one of many sources with such a model.  Another case is when there’s an existing supply of housing that is hard to get rid of in a short run while you can easily build more than existing supply.  All these thresholds can introduce "kinks to our curves:

Exhibit 8

If demand increases, increasing prices are a signal to suppliers to build, or upgrade space.
If demand falls, decreasing prices are a signal to suppliers to demolish, reduce maintenance, or convert space to some other use.


Question: does the supply response work equally well in both directions?

Exhibit 9

Here in Exhibit 9 we depart from two of our previous assumptions. We are looking at a single market and counting houses as before, but this time let’s assume that we are examining the stock of all houses, new and existing, instead of the flow of new construction. Let us further assume that over several years we can readily build some new houses, but we cannot so easily get rid of the old ones. This is not strictly true, of course; but experience teaches us that it does take time to reduce the stock of housing through depreciation and demolitions.

Suppose we started P0Q0. If demand shifts up and the price rises to P1, then developers build (Q1–Q0) new units, taking us to P1Q1. But if demand shifts down, the price falls to P3, as drawn we can’t get rid of the excess stock, and we are still stuck at Q3=Q0.

To the extent there is some elasticity in the downward direction, this effect will be mitigated. Rotate the bottom of the supply curve a little towards the origin while holding the pivot at P0Q0 fixed: the price decline is moderated, as the new Q3 (call it Q3' as you draw it in) falls a little bit below the old Q0.

In reality, declining cities like Detroit and Baltimore and Milwaukee often find it difficult to reduce the existing stock as rapidly as the decline and demand would suggest. That’s why there were periods in the 2000’s where you could find old but livable houses in parts of Detroit for $10,000 or less.

Time; and the Definition of the Good

Early models of real estate were focused on land markets in the land market was viewed as perfectly inelastic in supply. David Ricardo and Henry George were two of the authors who popularized these views of markets and their work is still extremely influential. On the face of it the Ricardo – George view seems plausible as in the words of attributed to a number of authors including Will Rogers and Mark Twain, “Son, buy land; they ain’t making any more of it.”

But in the 1940s, Richard Ratcliff pointed out that what was more often relevant is not the supply of land in the aggregate, but the supply of land for particular use; and this latter supply is  often at least somewhat elastic.

A moment’s introspection will suggest that the narrower we draw our definition of a market, the more elastic the supply. At one extreme, consider the total amount of land available on spaceship earth. It’s a pretty good approximation to treat that is fixed. When you examine a country, the same assumption is usually made; although the United States could always invade Canada or Mexico to increase our supply of land, that’s rare, and costly. (Recall that we have done so in the past although not the not in the last few hundred years; see Elliott. Cross your fingers that we are not about to do so again.)

Many projects add to the supply of land by draining swamps, infill projects and the like. Chicagoans know that Michigan Avenue used to be the Lake Michigan shoreline before infill using rubble from the Great Fire of 1871.  New York City residents are familiar with Battery Park City, for example, which was built on fill from the original World Trade Center excavation. Other famous examples of such land reclamation include projects hundreds of years ago in China and the Netherlands, to But again at the national level these are usually small enough to neglect, except in some of the smaller places like Singapore and Macau.  At the city level these may be important projects, but with a few possible exceptions (Holland?) they generally do not dominate basic outcomes in large national land markets.

A city’s land supply may also grow by annexing land from neighboring jurisdictions, as analyzed by Rusk. At the city level, growth through annexation and infill begins to become more relevant. But as we narrow the type of land use were considering, supply becomes more elastic still, as Ratcliff emphasized.  Suppose we consider not just the supply of land in Madison, but the supply of residential land. This can be increased not just by annexation or filling in part of Lake Monona, but also the much easier process of rezoning.

If we consider the supply of land for multifamily housing, the supply is more elastic still because it’s often easier to up-zone some single-family land to multifamily than it is to rezone agricultural and extend the infrastructure needed for multifamily to as yet undeveloped areas. In the most extreme case, the supply of land for your house or my house is more or less perfectly elastic. It’s unlikely that either of us is such a large part of market demand that we can affect the price. In other words, the supply curve we face as individuals is perfectly horizontal.  Thus Ratcliff demonstrated that the supply of land for particular uses can be very elastic even as the total supply of land for all uses is less so.

Sometimes the classical land model of Ricardo and George is a good approximation, when we examine the supply of land in the very short run, or at a very large level (e.g. a country).  Sometimes, it's not a good model, and we turn to Ratcliff and others.  We note in passing that followers of Henry George, take a very strong view, at times fanatical, that the classical model is relevant in all places and times, in all situations  Tant pis pour ils.  We'll discuss the pros and cons of "Henry George Theorems" at another point.

Thomas Carlyle Was Wrong About Slavery; and Also About Economics

This introduction hardly exhausts the topics of supply and demand, but you can see we've already exhausted the verbal capacities of most parrots.  The "Romantic" Thomas Carlyle was an influential writer in his day, still read and much cited but on this, as on slavery, he was over-matched against Enlightenment figures including 19th century economists like John Stuart Mill.

Concluding Thoughts

Exhibit 10

Whenever you draw, or view, supply and demand curves, ask:

• Which two variables – P and Q (most common), Q and Y (Engel curves), Q and demographics, etc. – are we drawing?
• Curves isolate 2 variables, ceteris paribus.  What are the other “demand shifters” and “supply shifters” in our supply and demand multivariate functions?
• Are we looking at stocks, or flows, of Q?
• How is the good defined?  How is price measured?
• What are the units of observation?  Are we looking at individual firms/consumers, or aggregating up to a market?  Are we looking at a submarket (e.g. rental demand only – is there a supply/demand for owner-occupants “off stage?”)
• Are we looking at the short run or the long run?  Can you put a time stamp on it?  (Overnight?  Six months?  A decade?)
• What do we know about the shape and the “steepness” of the curves?
• Are there related markets we should consider?

We've covered a lot of ground here, but there's much more to discuss in future posts, for example:

• How to measure price and quantity in the housing market. 
• Stocks and flows, in general equilibrium in the DiPasquale and Wheaton four-quadrant model. 
• How are expectations set in the housing market? 
• Key elasticities of supply and demand: what have we learned? 
• Consumer surplus, producer surplus, and their use in analyzing housing programs.

References Cited, and Further Reading

There are a number of references here, including some historical ones for those interested in tracing out the pedigree of the concepts.  But he basic concepts here -- supply, demand, equilibrium, elasticity -- have passed into the toolbox of "common knowledge" and can be used without reference to the original sources.  Some time ago, Pam Woodall, then editor of The Economist, forgot that when I sent her some basic material on supply and demand for a special center section on real estate.  She kindly recognized my contribution, but by labeling one of my charts that could be interpreted as exaggerating my contribution to economics.  Sharp-eyed friends were quick to mock what they claimed were my attempts to appropriate the work of giants, some of whom we've met in this post.

I come from a culture (Pennsylvania) where if you don't mock a friend at every opportunity, well, you're not much of a friend.

On the charge of claiming authorship of supply and demand, I plead innocence! 😉

Exhibit 11

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DiPasquale, Denise, and William C Wheaton. "The Markets for Real Estate Assets and Space: A Conceptual Framework." Real Estate Economics 20, no. 2 (1992): 181-98.

Edelstein, Robert H, and Desmond Tsang. "Dynamic Residential Housing Cycles Analysis." The Journal of Real Estate Finance and Economics 35, no. 3 (2007): 295-313.

Elliott, John Huxtable. Empires of the Atlantic World: Britain and Spain in America, 1492-1830. Yale University Press, 2007.

Fangyin, Zhou. "Between Assertiveness and Self-Restraint: Understanding China's South China Sea Policy." International Affairs 92, no. 4 (2016): 869-90.

Follain, James R. "A Study of the Demand for Housing by Low Versus High Income Households." Journal of Financial and Quantitative Analysis 14, no. 04 (1979): 769-82.

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George, Henry. Progress and Poverty: An Inquiry into the Cause of Industrial Depressions, and of Increase of Want with Increase of Wealth, the Remedy. W. Reeves, 1884.

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Goodman, John L. "Aggregation of Local Housing Markets." The Journal of Real Estate Finance and Economics 16, no. 1 (1998): 43-53.

Green, Richard K., and Stephen Malpezzi. A Primer on U.S. Housing Markets and Housing Policy.  American Real Estate and Urban Economics Association Monograph Series. Washington, D.C.: Urban Institute Press, 2003.

Green, Richard K., Stephen Malpezzi, and Stephen K. Mayo. "Metropolitan-Specific Estimates of the Price Elasticity of Supply of Housing, and Their Sources." American Economic Review 95, no. 2 (May 2005): 334-39.

Groenewegen, Peter. "Thomas Carlyle,‘the Dismal Science’, and the Contemporary Political Economy of Slavery." History of Economics Review 34, no. 1 (2001): 74-94.

Gyourko, Joseph. "Housing Supply." Annual Review of Economics. Vol. 1, no. 1 (2009): 295-318.

Hahn, Frank. "Auctioneer." The New Palgrave Dictionary of Economics: Volume 1–8  (2008): 271-74.

Hoeksema, Robert J. Designed for Dry Feet: Flood Protection and Land Reclamation in the Netherlands. American Society of Civil Engineers, 2006.

Humphrey, Thomas M. "Marshallian Cross Diagrams and Their Uses before Alfred Marshall: The Origins of Supply and Demand Geometry." Economic Review, no. Mar (1992): 3-23.

Jensen, Robert T, and Nolan H Miller. "Giffen Behavior and Subsistence Consumption." American Economic Review 98, no. 4 (2008): 1553-77.

Johnson, Douglas L, and Laurence A Lewis. Land Degradation: Creation and Destruction. Rowman & Littlefield, 2007.

Lauper, Cyndi and Harvey Fierstein.  Kinky Boots.  (2007) Broadway musical, based on Geoff Deane and Tim Firth (authors) and Julian Jarrold (director) 2005 film of the same name.

Malpezzi, Stephen. "Hedonic Pricing Models: A Selective and Applied Review." In Housing Economics and Public Policy: Essays in Honour of Duncan Maclennan, edited by Tony O. O'Sullivan and Kenneth Gibb, 67-89. Malden, Mass. and Carlton, Australia: Blackwell Science, 2003.

Malpezzi, Stephen, Larry Ozanne, and Thomas G. Thibodeau. "Microeconomic Estimates of Housing Depreciation." Land Economics 63, no. 4 (November 1987): 372-85.

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Mayo, Stephen, and Stephen Sheppard. "Housing Supply and the Effects of Stochastic Development Control." Journal of Housing Economics 10, no. 2 (2001): 109-28.

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Persky, Joseph. "Retrospectives: A Dismal Romantic." The Journal of Economic Perspectives 4, no. 4 (1990): 165-72.

Pollakowski, Henry O, and Traci S Ray. "Housing Price Diffusion Patterns at Different Aggregation Levels: An Examination of Housing Market Efficiency." Journal of Housing Research 8, no. 1 (1997): 107.

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Scitovszky, Tibor. "Some Consequences of the Habit of Judging Quality by Price." The Review of Economic Studies 12, no. 2 (1944): 100-05.

Seasholes, Nancy S. Gaining Ground: A History of Landmaking in Boston. MIT Press, 2018.

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Walters, Alan A. "Production and Cost Functions: An Econometric Survey." Econometrica (pre-1986) 31, no. 1, 2 (1963): 1.

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