Real Estate Economics

This research analyzes the dynamic properties of the difference equation that arises when markets exhibit serial correlation and mean reversion. We identify the correlation and reversion parameters for which prices will overshoot equilibrium (“cycles”) and/or diverge permanently from equilibrium. We then estimate the serial correlation and mean reversion coefficients from a large panel data set of 62 metro areas from 1979 to 1995 conditional on a set of economic variables that proxy for information costs, supply costs and expectations. Serial correlation is higher in metro areas with higher real incomes, population growth and real construction costs. Mean reversion is greater in large metro areas and faster growing cities with lower construction costs. The average fitted values for mean reversion and serial correlation lie in the convergent oscillatory region, but specific observations fall in both the damped and oscillatory regions and in both the convergent and divergent regions. Thus, the dynamic properties of housing markets are specific to the given time and location being considered.

This study is primarily an analysis of tradeoff between selling time and price, both on a nominal and real basis. Sellers are seen as desiring to maximize their discounted real selling price and trading off the nominal selling price with expected selling time. The time a property remains on the market is important, not only because of its reflection on price, but also because of its possible reflection on the issue of submarket equilibrium—an assumption in most urban price studies. The empirical results of this study shed light on how similar studies can easily misinterpret the implications of time on the market on price and how further work may be improved.

A two‐stage least squares model of housing prices is estimated with data collected from 3358 single‐family home transactions. The results provide evidence for an optimal marketing period and indicate that a liquidity premium is priced in single‐family home sales. Consistent with the hypothesis derived from economic search models, the model shows higher selling prices for houses having longer expected marketing periods. The model also shows a price premium for houses that sell faster than expectations. This effect supports the concept that liquidity is a value‐enhancing characteristic.

In the years surrounding the financial crisis, the share prices of equity Real Estate Investment Trusts (REITs) were much more volatile than the underlying commercial real estate prices. To better understand this phenomenon we examine the cross‐sectional dispersion of REIT returns during this time period with a particular focus on the influence of their capital structures. By looking at both the debt ratio and the maturity structure of the debt, we separate the pure leverage effect from the effect of financial distress. Consistent with leverage and financial distress costs amplifying the price decline, we find that the share prices of REITs with higher debt‐to‐asset ratios and shorter maturity debt fell more during the 2007 to early‐2009 crisis period. Although REIT prices rebounded with the bounce back in commercial real estate prices, financial distress costs had a permanent effect on REIT values. In particular, we find that REITs with more debt due during the crisis period tended to sell more property and issue more equity in 2009, when prices were depressed.

We study the impact of green building on loans in the CMBS market. A hazard model shows green buildings carry 34% less default risk, all else equal. A matched‐sample analysis gives similar results. We attribute the effect to a loan‐to‐value channel, where risk is lowered by a green price premium. The benefit comes at least partly from the level of green achievement, not only the label itself. Loans on buildings that were green at loan origination have slightly better terms than loans on nongreen buildings. That difference is growing over time, but the effect is economically small compared to default risk.

Home appraisals are produced for millions of residential mortgage transactions each year. In addition to preventing fraudulent transactions, an important benefit of appraisals when they report a value below the contract price is that they help borrowers renegotiate prices with sellers. However, appraised values are rarely below the purchase contract price: Some 30% of appraisals in our sample are exactly at the home price (with less than 10% of them below it). We construct a simple but intuitive model to explain how appraisers’ incentives within the institutional framework that governs mortgage lending lead to information loss in appraisals (i.e., appraisals set equal to the contract price). We also present new empirical findings relevant to the issue of appraisal accuracy, based on analysis of appraisal and contract price data and analysis of mortgage default patterns. One new finding—that the frequency of appraisal equal to contract price increases at the loan‐to‐value boundaries (notches) typical of mortgage pricing schedules—is, in fact, implied by our model. In addition, consistent with information loss or, more broadly, with the view that appraisals often artificially confirm the contract price, we find that mortgages with appraised value equal to the contract price are more likely to default.

Loan performance varies by default probability and loss given default (LGD). While the relationship between securitization and default probability has been studied intensively, the effect of securitization on LGD remains unexplored. Using a unique data set containing over 40,000 mortgage liquidations, this article studies the effect of private securitization on LGD. We document that securitized loans incur higher LGD than observably similar portfolio loans. The results indicate that the effect comes mainly from the difference in loan quality between securitized and portfolio loans. Securitized opaque mortgages incur more than 18% (in relative terms) higher loan losses than observably similar portfolio loans. In addition, a difference‐in‐difference analysis provides evidence that precrisis securitization standards contributed to higher loan losses.