Forecasting Prices and Excess Returns in the Housing Market

The paper uses quarterly indexes of existing single-family home prices estimated with microdata on properties that sold more than once to estimate excess returns to investment in owner-occupied housing. Housing prices and excess returns are estimated over the period 1970:1 to 1986:3 for Atlanta, Chicago, Dallas, San Francisco. Using time-series cross-section regressions we test for the forecastability of prices and excess returns using a number of independent variables. Price changes in one year tend to continue for more than one year in the same direction. The ratio of construction costs to price, changes in adult population and increases in real per capita income all are positively related to excess returns or price changes over the subsequent year. The results add weight to the argument that the market for single-family homes is inefficient.     

To Sell or Not to Sell: Measuring the Heat of the Housing Market

Combining list‐price, sale‐price and time‐on‐the‐market data, we estimate an index that summarizes housing market conditions and that has a direct economic interpretation. The index measures seller's bargaining power in a structural search model of home seller behavior. Structural estimation uncovers an analytical relationship between reduced form coefficients of hedonic and marketing‐time equations and structural parameters. Thus, the index can be estimated using individual‐level or aggregate data. Using housing transactions data from the Washington, D.C., area, we show that index trends coincide with the up and downturns in home appreciation rates and with popular perceptions about the “heat” of the market.     

The Effect of Mortgage Rate Buydowns on Housing Prices:

The model developed in this paper analyzes the effect of builder-financed FHA-VA mortgage subsidies or buydowns on the price of housing. Hedonic pricing equations are estimated for a locationally and qualitatively uniform sample of new tract development homes. The explanatory variables are vectors of physical and financial characteristics. The latter include a continuous variable for discount points paid by builders which is indicative of the magnitude of prepaid finance charges. The results indicate that a substantial portion of mortgage subsidy costs are shifted to buyers in the form of inflated housing prices.     

FHA Mortgage Discount Points, House Prices and Consumer Behavior

The allocation of FHA discount points between buyers and sellers has recently been the subject of empirical investigation. The research reported here examines this relationship in more detail. Regression equations were estimated for separate price categories of housing using dummy variables for the number of discount points being charged. Separate equations were also estimated for periods when points were rising and when they were falling. The results essentially are that sellers attempt to shift all of the discount points to buyers and frequently are successful. However, in certain situations the points are shared between buyers and seller.     

House Prices and Inflation

The present paper examines the long-run impact of inflation on homeowner equity by investigating the relationship between house prices and the prices of nonhousing goods and services, rather than return series and inflation rates as in previous empirical studies on the inflation hedging ability of real estate. There are two reasons for this methodological departure from prior practice: (1) while the total return on housing cannot be accurately measured, the total return on housing is fully reflected in housing prices, and (2) given that using returns or differencing a time series leads to a loss of long-run information contained in the series, valuable long-run information can be captured by using prices. Also, unlike previous related studies, we exclude housing costs from goods and services prices to avoid potential bias in estimating how inflation affects housing prices. Monthly data series are collected for existing and for new house prices as well as the consumer price index excluding housing costs for the period 1968–2000. Based on both autoregressive distributed lag (ARDL) models and recursive regressions, the empirical results yield estimated Fisher coefficients that are consistently greater than one over the sample period. Thus, we infer that house prices are a stable inflation hedge in the long run.     

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.     

Nonparametric Estimation of Dynamic Hedonic Price Models and the Construction of Residential Housing Price Indices

Parametric specifications for hedonic price equations are estimated using a data set from Alameda and San Francisco Counties and are compared to estimates using a nonparametric technique called locally weighted regression, LWR. LWR permits flexible estimation of the hedonic's curvature at median attributes and is less sensitive than standard regression techniques to the influence of unusual observations. The technique also avoids imposing a single functional form across time and municipalities. The LWR estimates of municipality-specific hedonics are then used to obtain implicit prices for housing attributes and to derive municipality-specific price indices. The results of extensive diagnostic checks of our technique are also reported.     

On Choosing Among House Price Index Methodologies

This paper compares housing price indices estimated using three models with several sets of property transaction data. The commonly used hedonic price model suffers from potential specification bias and inefficiency, while the weighted repeat-sales model presents potentially more serious bias and inefficiency problems. A hybrid model combining hedonic and repeat-sales equations avoids most of these sources of bias and inefficiency. This paper evaluates the performance of each type of model using a particularly rich local housing market database. The results, though ambiguous, appear to confirm the problems with the repeat sales model but suggest that systematic differences between repeat-transacting and single-transacting properties lead to bias in the hedonic and hybrid models as well.     

Housing Supply,Land Costs and Price Adjustment

We analyze relationships between housing supply elasticities, land costs and house price dynamics, contributing three main insights. First, higher housing supply elasticities help contain short‐run price spikes following demand shocks. Second, land price dynamics influence this relationship; supply responses are lessened and house price spikes are exacerbated as land prices increase. Third, we estimate a system of regional equations modeling housing supply using a Tobin's‐q specification (incorporating construction and land costs) and show that regional price dynamics are a function of the region's supply elasticity.     

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.     

Price transmission across the U.S. food distribution system

Price transmission models are estimated using recursive methods across 100 food commodities. From the individual commodities, short- and long-run price transmission coefficients are estimated for rising and falling prices. These coefficients are classified into five commodity categories and expressed as one vector while coding for causality, market levels, rising/falling prices, short and long run, and time period. Then the impact of each variable is estimated and inferences are drawn about the degree of price transmission. Results show that price linkages are strong but slightly declining over time, with some evidence of asymmetric behavior. Long-run rising prices are passed through more than long-run falling prices, except for fruits and vegetables.     

The Long‐Run Relationship Between House Prices and Rents

I use standard error‐correction models and long‐horizon regression models to examine how well the rent–price ratio predicts future changes in real rents and prices. I find evidence that the rent–price ratio helps predict changes in real prices over 4‐year periods, but that the rent–price ratio has little predictive power for changes in real rents over the same period. I show that a long‐horizon regression approach can yield biased estimates of the degree of error correction if prices have a unit root but do not follow a random walk, and I construct bootstrap distributions to conduct appropriate inference in the presence of this bias. The results lend empirical support to the view that the rent–price ratio is an indicator of valuation in the housing market.     

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.     

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.     

The Interaction of Building Codes and Housing Prices

The study analyzes the effect of restrictive building codes on the price of housing, and the simultaneous impact of housing values on the strictness of codes. A model is defined and estimated, using data for more than 1100 localities. The results show that strict codes raised housing values, in 1970, by about one thousand dollars. They furthermore show that the strictness of codes is in turn affected by housing values, as well as by the strength of construction unions. Homeowners and construction unions are thus both observed to gain from restrictive building codes, which can explain the prevalence of such regulations.     

Investor Confidence as a Determinant of China's Urban Housing Market Dynamics

China's urban housing market dynamics suggest that evolving investor confidence may be a relevant demand shifter. Such investors are continually updating their beliefs about the state of the macroeconomy and the policy uncertainty related to national and local housing policies. We build a 35 Chinese city real estate confidence index that varies over time and across cities. This index predicts subsequent house price appreciation and new housing sales. We document evidence of heterogeneous effects of investor confidence depending on a city's demographics and the city's elasticity of housing supply. Our results based on a new household‐level expectations survey bolster the case that investor expectations are an important determinant of real estate price dynamics.     

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.     

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.     

The Supply Curve for Housing

This article discusses the macro foundations of the supply curve for housing. The crucial points examined in the paper are the responsiveness of the supply curve to price, the independence of the supply curve from the demand curve for housing, and the stability of the supply curve. After the author discusses the theoretical foundations of the housing market, a small empirical model is constructed and estimated. Special attention is given to the identification problem connected with the estimation of the supply curve. The results are then examined and the within sample tracking ability of the model is discussed.     

The Economics of Style: Measuring the Price Effect of Neo‐Traditional Architecture in Housing

Vintage effects have received considerable attention from economists in the context of house prices. Although strongly related, the impact of architectural building styles on prices has not been studied yet. Using a cross‐sectional hedonic price analysis including building styles of recently developed homes in the Netherlands we find a significant price premium for housing with neo‐traditional architecture. Extensive intervention by local authorities on the supply side of the housing market seems the most probable explanation of this effect. The decreasing price premium over time reflects the impact of supply restrictions on price, but also indicates that style does matter.