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.     

Inflation and Housing Prices*

Home ownership is a claim on the stream of net rents. Like any income-producing asset, the market capitalizes its value. The price-rent multiple depends upon the expected growth rate of revenues and expenses, on financing terms, and on taxes. This paper derives this price-rent multiple in terms of these variables and calculates its value from 1963 through 1978. The results indicate that housing prices grew more rapidly than rents and the CPI largely as the result of a 33% increase in the price-rent multiple over those years. This increase in the capitalization rate occurred, despite higher nominal financing terms, because the relative terms of housing finance tended to ease and because the expected growth rate of rents increased more than its discount rate.     

The Value of Building Codes

We present a double moral hazard model to study the impact of building codes on the safety/quality level chosen by the builder and the level of care expended by the buyer. We show that codes can be a mechanism to implement the first-best solution provided: (1) The code prescribes the optimum safety level, (2) The code uses the efficient technology to achieve the optimum safety level, and (3) The courts use Negligence Rule as the liability rule. We argue, however, that codes in general do not use the efficient technology. We provide an alternative mechanism to implement the first-best solution. This mechanism is a contract between the builder and the buyer that specifies the level of safety jquality to be expended by the builder. We prove that the contract is a more effective and more efficient mechanism than are the building codes.     

Expected and Actual Effects of Housing Allowances on Housing Prices

Most analysts and policy makers expected fullscale housing allowance programs to substantially disturb local housing markets, causing housing prices tb increase sharply. This paper reviews conjectures about expected price effects, summarizes evidence from the Housing Assistance Supply Experiment, and explains why the program did not engender the expected price inflation.     

Effects of Housing Codes on Local Housing Markets

Housing codes are typically instituted in order to raise the average level of housing quality in a community. However, in doing so, the institution of a housing code likely has effects on other housing characteristics. Using data from municipalities in North Carolina, this study finds that municipalities with housing codes have higher average occupancy densities among all households and lower homeownership rates among low-income households, but housing codes have no statistically discernible effect on housing values and expenditures. The results suggest that housing codes are not costless; most importantly, codes force consumers to trade housing quantity for quality.     

Oil Prices and Urban Housing Demand

We develop a model of a monocentric, oil‐exporting city. The model predicts a “twist” (rotation combined with a level shift) of the house price gradient with an oil price change due to the combined producer price and transportation cost effects. Empirical findings support the predictions, with house price changes positively linked to the price of oil in cities specialized in oil and gas‐related industries, and negatively linked in suburban areas of all cities. These results quantify the large and differential risks to house prices associated with oil price changes both within and across cities. Overall, estimates suggest a 50% change in the price of oil results in a city‐wide house price change of 15% over five years in a city specialized in the production of oil (export employment share of 50%), whereas house prices for units greater than 15 miles from the city‐center change in relative terms by ?1.5% over the same period.     

Wages,Housing Prices and Commutes

Whereas economic theory suggests that, all else equal, workers should be willing to accept disamenities such as higher housing costs and longer commutes only if they are compensated with higher wages, little is known about the magnitude of these compensating differentials. In this article, I address this gap in the literature by estimating an empirical model of the relationship between wages, housing prices and commutes that addresses the simultaneous determination of these variables. The results from the empirical models suggest that the wage premia associated with high housing costs and long commutes are substantial. Furthermore, results from baseline models reveal that estimates of these compensating differentials are seriously biased if endogeneity is not addressed.     

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 Costs and Prices Under Regional Regulation

The regulation of development by regional agencies was proposed in the American Law Institute's Model Land Development Code. The objective of this additional regulation was to bring new development into accord with the growing concern with environmental degradation. The State of Florida was the first state to adopt its own version of the Code. This article reviews the housing cost and price impacts of this regulatory process as it has evolved in Florida. The empirical evidence suggests that both the cost and the price of new housing subject to this review are increased above that of housing exempted.     

Immigration,Capital Flows and Housing Prices

Research on immigration and real estate has found that immigrants lower house prices in immigrant destination neighborhoods. In this article, we find that this latter result is not globally true. Rather, we show that immigrants can raise neighborhood house prices, at least in the case of the wealthy immigrants that we study. We exploit a surprise suspension and subsequent closure of a popular investor immigration program in Canada to use a difference‐in‐differences methodology comparing wealthy immigrant destination census tracts to nondestination tracts. We find that the unexpected suspension of the program had a negative impact on house prices of 1.7–2.6% in the neighborhoods and market segments most favored by the investor immigrants. This leads to an approximate lower bound on the effect of capital inflows of 5%.     

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.     

The Effect of Regional Growth Characteristics on Regional Housing Prices

As contrasted with strictly national housing reports, this article highlights the regional variations in population growth patterns and recently built owner-occupied housing as a means of determining single-family housing price components (i.e., developed lot values, homebuilding costs, and builder's profit and overhead) by region. The assertion that escalating lot costs and increases in new housing costs will limit the demand for single-family housing is challenged on a national basis and treated individually for the West, Northeast, South, and North Central Regions of the country.     

上海市住宅价格影响因素及未来走势研究

本文采用实证分析方法,以2000~2009年上海市宏观经济数据为样本选取上海市33个经济社会指标,通过主成分分析得到影响上海市商品住宅价格的4个主成分并以指标载荷得分为其命名,即:需求因素,供给因素,市场因素,土地因素。选取主成分代表指标,通过多元回归方法求出其与上海市商品住宅价格的回归方程,并以此为依据进行预测分析。分析结果显示,需求因素为影响上海市住宅房价的最主要因素,供求矛盾推动了上海市商品住宅价格的持续走高,并在未来一段时间仍然保持上升趋势。文章最后从政府监管和需求者角度分别给出了相关建议。     

Housing Regulation,Externalities and Residential Property Prices

This article examines the effects of quantity restrictions on residential property prices in the presence of neighborhood externalities. A Brigham Young University policy limiting students’ location choices provides a natural experiment for studying the externality and quantity restriction effects on property values. A flexible hedonic model is used to control for nonstudent population spatial sorting by type. The estimates show significant positive quantity restriction and student agglomeration effects on student housing prices. There are also significant differences in the negative student externality across nonstudent neighborhoods, with the quantity restriction reinforcing (offsetting) the student price premium (discount) at the boundary.     

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.     

The Impact of Growth Control Regulations on Housing Prices in California*

This paper examines the impact of growth controls on the price of new single-family homes. Four types of growth controls are discussed and each is found to have a significantly different impact on housing prices. Regulations that are imposed by one locality only are first compared to those that are imposed by a locality whose neighbors also control growth. In California, increases in house prices in communities with only local growth controls cannot be distinguished from communities that do not control growth. However, the 1969 to 1976 housing price increase in growth control jurisdictions located in extensively regulated housing markets is significantly higher than in local-only or no-control jurisdictions. In addition, controls that restrict the rate of development are compared to those that specify the quality of development. In the extensively regulated San Francisco Bay area, the 1969 to 1976 housing price increase was 35% higher in rate-controlled communities and 20% higher in quality-controlled communities than in no-control communities.     

The Long-Run Relationship between House Prices and Income: Evidence from Local Housing Markets

Many in the housing literature argue that house prices and income are cointegrated. I show that the data do not support this view. Standard tests using 27 years of national-level data do not find evidence of cointegration. However, standard tests for cointegration have low power, especially in small samples. I use panel-data tests for cointegration that are more powerful than their time-series counterparts to test for cointegration in a panel of 95 metro areas over 23 years. Using a bootstrap approach to allow for cross-correlations in city-level house-price shocks, I show that even these more powerful tests do not reject the hypothesis of no cointegration. Thus the error-correction specification for house prices and income commonly found in the literature may be inappropriate.     

Internet Search Behavior,Liquidity and Prices in the Housing Market

We employ detailed internet search data to examine price and liquidity dynamics of the Dutch housing market. We show that the number of clicks on properties listed online proxies demand and the number of listed properties proxies supply. From this internet search behavior, we create a market tightness indicator and we find that this indicator Granger causes changes in both house prices and housing market liquidity. The results of a panel VAR suggest that a demand shock results in a temporary increase in liquidity and a permanent increase in prices in urban areas. This is in accordance with search and matching models.     

Long‐Term Growth in Housing Prices and Stock Returns

A firm's long‐term stock returns are negatively related to past growth in housing prices in the state where the firm is located. The housing price effect is persistent and robust to controlling for the long‐term stock return reversal effect, changes in mortgage interest rates across the states, cyclicality in housing prices and overall local economic conditions. There is no evidence that extant asset pricing models can adequately explain the effect. The study discusses potential explanations for, and the implications of, the cross‐regional housing price effect.