Risk and Return in the U.S. Housing Market: A Cross-Sectional Asset-Pricing Approach

This article carries out an asset-pricing analysis of the U.S. metropolitan housing market. We use ZIP code–level housing data to study the cross-sectional role of volatility, price level, stock market risk and idiosyncratic volatility in explaining housing returns. While the related literature tends to focus on the dynamic role of volatility and housing returns within submarkets over time, our risk–return analysis is cross-sectional and covers the national U.S. metropolitan housing market. The study provides a number of important findings on the asset-pricing features of the U.S. housing market. Specifically, we find (i) a positive relation between housing returns and volatility, with returns rising by 2.48% annually for a 10% rise in volatility, (ii) a positive but diminishing price effect on returns and (iii) that stock market risk is priced directionally in the housing market. Our results on the return-volatility-price relation are robust to (i) metropolitan statistical area clustering effects and (ii) differences in socioeconomic characteristics among submarkets related to income, employment rate, managerial employment, owner-occupied housing, gross rent and population density.     

Explaining Intermetropolitan Variation in Housing Prices, Rents and Land Prices

In attempting to explain why housing prices, rents and urban land prices vary so dramatically between U.S. metropolitan areas, a simple model of a metropolitan housing market is presented identifying three interrelated submarkets. Estimating equations for rent, housing prices and urban land prices are identified and estimated using two-stage least squares. The empirical results provide strong support for the theoretical model concerning how these three submarkets interact. The results also suggest that household income and construction costs are the most important factors causing housing prices, rents and land prices to vary between metropolitan areas.     

How Well Do Section 8 FMRs (Fair Market Rent) Match the Cost of Rental Housing? Data from Thirty-Nine Large Cities

The FMR (Fair Market Rent) idea was built into the Section 8 lower income rental assistance program to provide a means to reflect variations in the rental cost of comparable housing in different market areas and also rental cost differences within a market area due to size variations in housing units. Does the FMR schedule for the Section 8 Existing Housing Program parallel the actual differences in the rents of comparable housing among metropolitan areas? We find the 1975 schedule fulfills some of the criteria for an efficient and equitable Section 8 Existing Housing Program but not all.     

Housing Price Indexes from the 1974–1983 SIMSA Annual Housing Surveys

This paper reports residential real estate price indexes computed from the Standard Metropolitan Statistical Area (SMSA) Annual Housing Survey (AHS) for the 1974 through 1983 period. During this ten-year period, the U.S. Bureau of the Census conducted detailed surveys of the housing stock in sixty metropolitan areas in a three to four year cycle. This information is used to compute tenure specific hedonic housing price indexes for: (1) the entire metropolitan housing market; (2) separately for properties located in the central city and in the suburbs (whenever central city locations are identified); and (3) for three points in the dwelling quality distribution-for substandard housing (using the definition employed by the U.S. Department of Housing and Urban Development), for new housing (housing less than three years old and not substandard), and for existing standard quality housing (everything else). In addition, the hedonic prices reported here are adjusted for the finite sample bias introduced when taking the exponential of a lognormally distributed random variable.     

A Vacancy Transfer Model of the Structure of the Local Housing Market

Household mobility data derived from vacancy chain or turnover studies may be used to develop a Markov model of the local housing market. Such a model based on empirical data from the Detroit metropolitan area indicates a strong tendency for households to substantially increase their housing expenditures when they move. This finding is consistent with the filtering model of local housing market dynamics. However, these data do not support the hypothesis that more expensive new housing will produce the greatest number of indirect housing opportunities. New units at every cost level generated about the same total of turnover vacancies. The model also indicates that all types of new construction ultimately have their greatest impact on lower cost housing in established communities.     

The Asset Approach to Pricing Urban Land: Empirical Evidence

Many papers have attempted to explain Intelmetropolitan variations in the price of housing using multi-equation models of the metropolitan housing market. This paper uses a long-run equilibrium urban asset model to explain such variations. The model builds upon previous models that introduce uncertainty into the dynamic urban model of land conversion. The empirical results strongly support the asset approach to valuing land in urban areas.     

Rental Housing Markets and the Natural Vacancy Rate

This paper employs new census vacancy rate data to analyze the price-adjustment mechanism for rental housing. The study extends previous research on this topic, which provided conflicting evidence concerning the traditional theory of rental housing market adjustment (see Smith [10] , [11] ; DeLeeuw and Ekanem [2] ; Eubank and Sirmans [4] ; and Rosen and Smith [8] ). Cross-section and time-series data are pooled to estimate natural vacancy rates for sixteen United States cities for the 1981–85 period. The analysis further explores the determinants of variation in natural vacancy rates across those metropolitan areas.     

An Evaluation of Hedonic Price Indexes for Thirty-four Large SMSAs

The purpose of this paper is twofold. First, a set of price indexes are developed for thirty-four metropolitan areas for the years 1975 and 1978. Indexes are also broken down by central city and suburb for twenty two of the metropolitan areas. These indexes measure variation in the price per unit of owner-occupied housing stock and the price per unit of housing services of rental housing. Confidence intervals are constructed for the price indexes. It is found that the indexes are reasonably precise measures in that the typical ratio of the confidence interval to the predicted median rent or value is about 13%. Also, analysis of the confidence intervals indicates substantial and statistically significant variation in the price of housing stock and services among the metropolitan areas studied. One of the major benefits of this set of indexes is that it can be used to address one of the most important questions in real estate — Why do housing prices and rents vary among metropolitan areas and over time? Research projects are currently underway that use the data to address these two questions.     

Marking Single–Family Property Values to Market

This paper is the text of the 2001 Presidential Address for the American Real Estate and Urban Economics Association. House price prediction accuracy is a function of: (1) the procedure used to identify within–metropolitan–area housing submarket boundaries, (2) the econometric technique used to estimate the parameters of the house price model and (3) the characteristics of the local housing market. This paper empirically examines several procedures for delineating within–metropolitan–area housing submarket boundaries. In addition, the paper examines the increase in prediction accuracy that can be achieved by employing spatial econometric techniques to spatially adjust ordinary least squares house price predictions. Finally, the paper examines the influence that local housing market characteristics have on house price prediction accuracy. Housing market characteristics examined here are: (1) structural characteristics of neighborhood properties, (2) heterogeneity of the neighborhood housing stock and (3) housing market liquidity. Prediction accuracy is examined using hedonic house price equations with over 40,000 single–family transactions for Dallas, Texas.     

Return and Volatility Transmission in U.S. Housing Markets

This article uses the Case‐Shiller U.S. Home Price Indices to analyze spatial dependencies across 16 metropolitan markets for the period January 1989 to June 2006. Return transmission patterns establish New York, San Francisco and Miami as among the most influential markets. In terms of volatility linkages, there is a considerable amount of transmission in the East between New York, Boston and Washington, DC, and innovations in the housing markets of Miami, Los Angeles and San Francisco play an influential role within their respective regions. In comparison, markets in the Central and Mountain regions appear to be relatively independent from external influences. Overall, the linkages appear to be more intensive during the active phase of the real estate market (1999–2006) than during the calm phase (1989–1998).     

Modeling Spatial Variation in Housing Prices: A Variable Interaction Approach

The absolute location of each real estate parcel in an urban housing market has a unique location-value signature. Accessibility indices, distant gradients and locational dummies cannot fully account for the influence of absolute location on the market price of housing because there are an indeterminable number of externalities (local and nonlocal) influencing a given property at a given location. Furthermore, the degree to which externalities affect real estate values is not only unique at each location but highly variable over space. Hence, absolute location must be viewed as interactive with other determinants of housing value. We present an interactive variables approach and test its ability to explain price variations in an urban residential housing market. The statistical evidence suggests that the value of location, as embodied in the selling price of housing units, may not be separable from other determinants of value. It is recommended that housing valuation models, therefore, be specified to allow site, structural and other independent attributes to interact with absolute location—{ x , y } coordinates—when accounting for intraurban variation in the market price of residential housing. This approach is especially useful when estimating the value of housing for geographic areas where very little is known a priori about the neighborhoods or submarkets.     

Consumer Preferences in Housing Market Analysis: An Application of Multidimensional Scaling Techniques

The value of single–family housing is a function of demographic, economic and psychographic variables. Much work has been done on the objectively measurable criteria, but virtually none on the subjective, perceptual criteria. This study uses consumer-supplied similarity measures of housing alternatives to develop a multidimensional perceptual "map" of the way consumers view housing choices. The dimensions of this perceptual space represent the evaluative criteria utilized, consciously or unconsciously, during the housing evaluation process. The analysis, although exploratory in nature, suggests that all perceptually defined market segments use the same evaluative construct; that market segments may be defined in terms of differences in the relative importance of each criterion; that market segments do not necessarily correspond to simple demographic measures; and that the major evaluative criteria in the perceptual process generally agree with those found in the literature based on objective measurements.     

Search Benefit in Housing Markets: An Inverted U‐Shaped Price and TOM Relation

The nature of the relationship between a property's selling price and its marketing time in the housing market remains an open question to date, despite almost 40 years of inquiry and hundreds of regressions conducted on various data sources. This study attempts to settle the long‐standing open question by examining the issue from a new perspective. We demonstrate that the true price–TOM relationship should be nonlinear and characterized by an inverted U‐shaped curve wherein the selling price increases with TOM up to a certain threshold, reflective of a positive exposure effect and decreases thereafter to reflect a negative stigma effect. This relationship is borne out in an empirical analysis using a large sample of home sales from the Hampton Roads, Virginia metropolitan area during an extended period of time. We then formulate hypotheses about the benefit of search by home sellers, which are subsequently confirmed by the empirical findings.     

Amenity‐Based Housing Affordability Indexes

The recent slump notwithstanding, substantial increases in house prices in many parts of the United States have served to highlight housing affordability for moderate‐income households, especially in high‐cost, supply‐constrained coastal cities such as Boston. In this article, we develop a new measure of area affordability that characterizes the supply of housing that is affordable to different households in different locations of a metropolitan region. Key to our approach is the explicit recognition that the price/rent of a dwelling is affected by its location. Hence, we develop an affordability methodology that accounts for job accessibility, school quality and safety. This allows us to produce a menu of town‐level indexes of adjusted housing affordability. The adjustments are based on obtaining implicit prices of these amenities from a hedonic price equation. We thus use data from a wide variety of sources to rank 141 towns in the greater Boston metropolitan area based on their adjusted affordability. Taking households earning 80% of area median income as an example, we find that consideration of town‐level amenities leads to major changes relative to a typical assessment of affordability.     

基于挤出效应的住房保障规模对商品住房价格的影响

中国房地产市场中保障性住房的角色和作用愈加重要,保障性住房建设规模正在逐年扩大.商品住房价格变化是供给需求作用的结果,但是随着保障房供给规模的增加,商品住房价格的涨幅也会发生改变,说明保障房的供给规模会对普通商品房的供需产生影响.本文以北京市商品住房市场为例,选取住宅需求、住宅供给、住房投机对价格的影响为自变量,住宅价格为因变量,利用多元回归模型对商品住房价格变化进行实证研究,同时通过考虑保障房供给对住房市场产生的不同挤出效应,分析保障房供给规模供需变化对房价产生的影响,进而为政府制定相关的住房保障政策提供建议和对策.     

The Effect of Housing Market Disequilibrium on the Supply of Labor: Evidence from Poland, 1989–1990*

This paper examines the effect of housing market disequilibrium on the supply of labor. Earlier studies suggested that housing market disequilibria affected other markets through altered consumption patterns (Podkaminer 1982 , 1988 ) or because housing shortages restricted labor mobility (Mayo and Stein 1988 ). This paper examines the disincentive to supply labor which arises from housing market disequilibrium. The disequilibrium is measured by four variables. One is a measure of density, while the others are based upon the number of persons in official housing queues. Each of the variables is negative and significant in the two-stage least squares estimates of labor supply. Estimates disaggregated by skill-class are also presented. Housing market disequilibrium has the greatest impact on the labor supply of the least skilled.     

Forecasting Housing Markets: Lessons Learned

Forecasting changes in housing finance (instruments and institutions), housing demand (tenure, quantity, and real price), and housing production requires forecasting numerous exogenous factors—inflation, government policy, and demographic forces being the most obvious—and interpreting their impacts on the housing market. We use forecasts made over the last two decades to illustrate the importance of these variables and of interpreting their impacts appropriately.     

Housing Return and Construction Cycles

This paper presents a general equilibrium model of the residential housing market. Within the model housing returns, housing construction, mortgage loan terms, and household maintenance behavior are all endogenous. These interacting elements tie expected housing returns to expected changes in family wealth. As a result: (1) families are credit constrained; (2) mortgage loan-to-value ratios can be used to forecast future housing returns; (3) developers acquire land when expected housing returns lie above the rate of interest and then develop when housing returns lie below. Thus, their holdings and construction decisions also forecast housing returns.     

Nature of comovements in US state and MSA housing prices

This study analyzes the time-varying nature and determinants of comovements in US housing prices using state and metropolitan statistical area (MSA) data. We employ dynamic factor models with time-varying loadings and stochastic volatility (DFM-TV-SV) to estimate the national, regional, and state factors. The time-varying factor loadings and stochastic volatility features enrich the dynamic factor model structures and are an effective tool to examine the comovements in housing prices. We find that the national factor is the dominant factor in explaining the movement of housing prices. The national factor accounts for 79% of the variation in state-level housing prices on average, with the greatest magnitude occurring during the housing boom and bust periods in many regions and states. We also find that the factors and synchronization effects are time-varying and heterogeneous across regions. The state-level housing prices contain higher national housing factor components in states with more diverse economies, higher wages and house prices, and lower unemployment rates. These findings shed light on the effectiveness of residential real estate diversification across the United States and the potential for elevated national housing risk amid economic downturns due to increased national housing price integration.     

New Place-to-Place Housing Price Indexes for U.S. Metropolitan Areas, and Their Determinants

Housing prices vary widely from market to market in the United States. The purpose of this study is to (1) construct new place-to-place indexes of the price of housing, using the 1990 Census, and (2) analyze the determinants of housing prices, with a particular focus on the supply side determinants—regulatory and natural constraint—as well as the usual demand determinants.