Do Conspicuous Consumers Pay Higher Housing Premiums? Spatial and Temporal Variation in the United States

This study is the first to examine the relationship between conspicuous demand and housing price dynamics. We hypothesize that conspicuous consumers would want high‐end homes to signal their wealth and this housing consumption behavior would induce greater deviations from fundamental house prices. We test this by using a unique dataset that matches the consumers’ appetite for nonhousing luxury goods from Google Insights for Search to housing premiums that they pay for high‐end houses in U.S. Metropolitan Statistical Areas (MSAs) during 2004–2011. The estimation results demonstrate that controlling for a wide range of MSA demographic and economic characteristics, conspicuous demand has a significant, positive relationship with housing premiums. This relationship varies spatially and temporally. Conspicuous demand has a stronger relationship with a price increase in high‐end homes in MSAs with a steady, higher housing premium than in MSAs with a volatile, lower premium during the boom period. In MSAs with a steady, higher housing premium, the relationship remains significant even during the bust period, potentially contributing to maintaining higher housing premiums.     

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.     

Canceling the admission priority of private schools enlarges housing price gap in public school districts: Evidence from Shanghai's new admission policy

In 2018, Shanghai implemented an admission policy that canceled the admission priority of private schools to promote education equity. Since this policy is a unique measure for adjusting private and public school competition and discouraging private school choice priority, little is known about the policy effects. In this research, to examine the impact of the new admission policy on the capitalization of public education quality, we apply boundary fixed effect and Difference in Differences (DID) analysis to housing transaction records before and after the policy. The admission policy on average led to an additional 2% housing price premium for every standard deviation increase in public school quality. However, this average increase in premium was mainly driven by elite (top 5%) school districts, where an additional 8.6% housing price premium was generated by the policy. Housing prices in nonelite school districts, on the other hand, demonstrated no significant changes. These results indicate that the policy enlarges the housing price gap among school districts with different education quality. Thus, rather than promoting education equity, this policy may overall worsen the housing affordability in good public-school districts and make access to quality education more exclusive.     

An Empirical Examination of Traditional Neighborhood Development

This study analyzes the impact of the new urbanism on single-family home prices. Specifically, we explore the price differential that homebuyers pay for houses in new urbanist developments relative to houses in conventional suburban developments. Using data on over 5,000 single-family home sales from 1994 to 1997 in three different neighborhoods, hedonic regression results reveal that consumers pay more for homes in new urbanist communities than those in conventional suburban developments. Further analyses indicate that the price premium is not attributable to differences in improvement age and other housing characteristics.     

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.     

The Capitalization of School Quality into Renter and Owner Housing

Residential property amenities including school quality should be capitalized into both rent and property sale prices. Evidence of price and rent premiums for higher school quality is provided. The price premium for school quality for owners exceeds the premium for renters. The premiums paid by renters and owners vary with the likelihood the household directly uses school services, housing market conditions, whether the property is in an urban or suburban area and by the observed school quality in the years leading to the transaction. The larger price premium paid by owners is supported by enhanced liquidity and tempered price volatility for properties located in quality school districts.     

The Foreclosure–House Price Nexus: A Panel VAR Model for U.S. States, 1981–2009

Despite housing's economic importance, little has been written on how foreclosures and home prices interact in a framework that includes macroeconomic and housing variables such as employment, permits or sales. Panel VAR results for quarterly state‐level data indicate that price–foreclosure linkages run both ways. Foreclosures negatively impact home prices. The negative impact of prices on foreclosures, however, is much larger. These results suggest the low‐frequency association observed between foreclosures and prices is mostly driven by the endogenous adjustment of foreclosures to prices via the strategic choices of homeowners and lenders, rather than through the effects of foreclosures on home prices.     

Search and Liquidity in Single-Family Housing

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.     

Graduated Flood Risks and Property Prices in Galveston County

Our hedonic property analysis approach in Galveston County, Texas aims at estimating the impacts of flood risks and water‐related amenities in a more systematic way. First, we interact distance to the nearest coastline and flood risk in order to account for these impacts acting together on housing sales prices in our coastal community. Second, we use more granular flood risk measure in the analysis compared to the existing literature. Results show that the hedonic price effect is dependent upon the distance to the nearest coastline, and as expected the distance effect varies by flood risk type. We find that in this coastal housing market properties located in the highest risk flood area, for up to nearly a quarter mile from the nearest coastline, actually command a price premium. A recent movement toward risk‐based flood insurance premiums in the United States was deeply opposed by the real estate sector for fear of causing property values to steeply decline. This analysis sheds some further light on this depressed property value assertion highlighting its sensitivity to distance to the water.     

House Prices and Regional Real Estate Cycles: Market Adjustments in Houston

Real estate cycles often generate sharp swings in real housing prices, price changes that cannot be adequately described by a single statistic such as median home values. Instead, the entire structure of prices across all quality levels must be examined. This paper analyzes the price impact of the Houston, Texas, real estate bust. It shows that the average price of housing fell, and that the structure of the housing price function itself changed. Changes in the marginal price of housing were probably more significant to the market equilibrating process than the decline in average price alone.     

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.     

On the Nature of Self‐Assessed House Prices

In models of optimal household behavior, the value of housing affects consumption, savings and other variables. But homeowners do not know the value of their house for certain until they sell, so while they live in their home they must rely on local house price data to estimate its value. This article uses data from the recent housing boom and bust to demonstrate that changes in households' self‐assessed home values are strongly consistent with the predictions of a model in which households optimally filter available house price data. Specifically, we show that self‐assessed house prices did not increase as rapidly as house price indexes during the boom and did not decline as severely during the bust. A Kalman filter model nearly perfectly replicates these data. These findings have direct implications for economists studying asking prices during booms and busts, optimal default decisions and other key housing‐related phenomena.     

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.     

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.     

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.     

Effects of Macroeconomic Uncertainty and Labor Demand Shocks on the Housing Market

This article shows that macroeconomic uncertainty affects the housing market in two significant ways. First, uncertainty shocks adversely affect housing prices but not the quantities that are traded. Controlling for a broad set of variables in fixed‐effects regressions, we find that uncertainty shocks reduce both housing prices and median sales prices in the amount of 1.4% and 1.8%, respectively, but the effect is not statistically significant for the percentage changes of all homes sold. Second, when both uncertainty and local demand shocks are introduced, the effects of uncertainty on the housing market dominate that of local labor demand shocks on housing prices, median sale prices, the share of houses selling for a loss and transactions. The aforementioned effects are largest for the states that exhibit relatively high housing price volatilities, suggesting real options effects in the housing market during the times of high uncertainty.     

Substitutability and Complementarity of Urban Amenities: External Effects of Built Heritage in Berlin

This article analyzes the impact of designated landmarks on condominium transaction prices in Berlin, Germany. We test for price differentials between listed and nonlisted properties and study their impact on surrounding property prices. The proximity to built heritage is captured by the distance to listed houses and heritage potentiality indicators. Impact is assessed by applying a hedonic model to microlevel data, and this process also addresses spatial dependency. While our findings suggest that designated landmarks do not sell at a premium or discount, landmarks are found to have positive external effects on surrounding property prices within a distance of approximately 600 m.     

User Cost and the Demand for Housing Attributes

A number of studies have related changes in the demand for housing to changes in user cost. All have treated housing as a composite good rather than as a bundle of characteristics. We consider the effect of changing user cost on the demand for the component characteristics of owner-occupied housing, and, given information about the supply of the characteristics, we predict implicit price responses. An empirical test of our model indicates that reductions in user cost result in higher real prices for the non-replicable attributes of housing, examples being location and access to fixed amenities. In contrast, the price of attributes that are perfectly elastic in supply are not affected by changes in user costs. We conclude that the effects of changing user cost are not uniform across housing types and locations, thus generating the appearance of housing submarkets.     

The Effects of Charm Listing Prices on House Transaction Prices

As is the case for many different goods and services, it is common practice in many real estate markets for sellers to offer properties for sale at listing prices just below some round number price ( e.g. , $99,900 instead of $100,000). The academic marketing literature refers to this practice as "charm" pricing and suggests that this strategy is an attempt by sellers to take advantage of buyers' cognitive processes in which charm prices affect buyers' perceptions about the seller or the item being offered for sale. Although numerous papers in the housing economics literature have addressed the impact of the magnitude of listing price on observed house transaction prices, no prior published study has considered the impact of the design of listing prices in housing markets. This paper presents an empirical investigation of the effects of charm pricing on house transaction prices using sample data. The results provide some evidence that houses listed at certain charm prices sell for significantly greater transaction prices than those listed at round number prices.     

Tax Limits and Housing Markets: Some Evidence at the State Level

Property tax limitations, as well as other tax and expenditure restrictions on state and local governments in the United States, date back to the late 19th century. A surge in property tax limitation legislation occurred in the late 1970s and early 1980s, and its effects on government revenue, school financing and educational quality have been studied extensively. However, there is surprisingly little literature on how property tax limits affect housing markets. For the first time, we examine the impacts of property tax limitations on housing growth, in addition to their impacts on housing prices. Using state‐level data over 23 years, we find that property tax limits increase housing prices (indexes) by approximately 2%. Property tax limits appear to have little impact on the growth in the housing stock, but education spending limits reduce the number of building permits by over 6%. Our indirect evidence suggests that the number of housing units may grow when property tax limits are accompanied by increases in other own‐source revenues to state government.