The Value of Access to Rail Transit in a Congested City: Evidence from Housing Prices in Beijing

Using unique data sets of Beijing's congestion patterns and housing prices, I find that consumers are willing to pay significantly more for access to rail transit in more congested areas. Transit accessibility, however, offers little travel advantage outside of dense urban areas. The expansion of the metro network mitigates the costs of road congestion, creating both private and social benefits. Two policy initiatives aimed at reducing congestion are found to have achieved positive value effects. Further analysis reveals heterogeneous demand for accessibility, with wealthier residents and those owning fewer cars paying a higher premium for access to rail transit.     

Reaction of House Prices to a New Rapid Transit Line: Chicago's Midway Line, 1983–1999

This study examines the effect of the new rapid transit line from downtown Chicago to Midway Airport on single-family house prices before and after the opening of the line. The results show that the housing market anticipated the opening of the line. House prices were being affected by proximity to the stations in the late 1980s and early 1990s—after the plans for the line were well known. The difference between the increase in the value of homes within the sample area as compared with properties farther away from the new transit stations was approximately $216 million between 1986 and 1999.     

Transportation, Sorting and House Values

In this paper, we examine the importance of accessibility to employment and transportation system attributes for residential location choice, car ownership and house values. Using the 1980 Census of Housing and Journey to Work data merged with transportation system data, we find strong evidence of residential sorting based on employment location. We find that suburban areas with good commuter rail access to the CBD have significantly greater fractions of their labor force working in the CBD, own fewer cars and have higher house prices than similar neighborhoods and houses in census tracts without service. The house value premium is over 6.4%.     

Analysis of spectrum auctions in India—An application of the opportunity cost approach to explain large variations in spectrum prices

The remarkable growth of mobile communication has reinforced the significance of the radio spectrum for mobile network operators. The availability of spectrum varies considerably between different countries due to national regulatory decisions. The focus in this paper is on India where operators have access to a limited amount of spectrum. This paper analyses the value of spectrum by estimating the opportunity cost, which is calculated by the savings that can be achieved by acquiring appropriate amount of spectrum rather than investing in additional base stations. The applied approach combines network deployment, user demand levels, cost, and capacity issues, which are integrated in the application in the opportunity cost approach for spectrum. The opportunity cost of spectrum is compared with prices paid at spectrum auctions. The analysis includes a discussion of drivers that determine the willingness to pay for spectrum. The results show that the opportunity cost of spectrum in relation to auction prices is lower than prices operators paid for 3G spectrum in the metro circles (service areas) while the value derived from the opportunity cost is higher than auction prices in the remaining circles.     

Gentrification Trends in New Transit-Oriented Communities: Evidence from 14 Cities That Expanded and Built Rail Transit Systems

Over 25 billion dollars were spent between 1970 and 2000 in 14 major cities in the United States on the construction of new rail transit lines. This massive investment in rail transit construction and expansion allows me to study the consequences of local public goods improvements for communities nearby new stations. This article uses a 14-city census tract–level panel data set covering the years 1970 to 2000 to document significant heterogeneity in the effects of rail transit expansions across the 14 cities. Communities receiving increased access to new "Walk and Ride" stations experience greater gentrification than communities that are now close to new "Park and Ride" stations.     

Estimating the Gains from New Rail Transit Investment: A Machine Learning Tree Approach

Urban rail transit investments are expensive and irreversible. As people differ with respect to their demand for trips, their value of time, and the types of real estate they live in, such projects are likely to offer heterogeneous benefits to residents of a city. Defining the opening of a major new subway in Seoul as a treatment for apartments close to the new rail stations, we contrast hedonic estimates based on multivariate hedonic methods with a machine learning (ML) approach. This ML approach yields new estimates of these heterogeneous effects. While a majority of the “treated” apartment types appreciate in value, other types decline in value. We cross‐validate our estimates by studying what types of new housing units developers build in the treated areas close to the new train lines.     

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.     

Predicting Spatial Patterns of House Prices Using LPR and Bayesian Smoothing

This article is motivated by the limited ability of standard hedonic price equations to deal with spatial variation in house prices. Spatial patterns of house prices can be viewed as the sum of many causal factors: Access to the central business district is associated with a house price gradient; access to decentralized employment subcenters causes more localized changes in house prices; in addition, neighborhood amenities (and disamenities) can cause house prices to change rapidly over relatively short distances. Spatial prediction (e.g., for an automated valuation system) requires models that can deal with all of these sources of spatial variation. We propose to accommodate these factors using a standard hedonic framework but incoporating a semiparametric model with structure in the residuals modeled with a partially Bayesian approach. The Bayesian framework enables us to provide complete inference in the form of a posterior distribution for each model parameter. Our model allows prediction at sampled or unsampled locations as well as prediction interval estimates. The nonparametric part of our model allows sufficient flexibility to find substantial spatial variation in house values. The parameters of the kriging model provide further insights into spatial patterns. Out–of–sample mean squared error and related statistics validate the proposed methods and justify their use for spatial prediction of house values.     

Are Rising College Premiums Capitalized into House Prices? Evidence from China

Many areas in China experienced steeply rising house prices beginning in 2003. We test whether a change in local residency requirements may have played a role in driving up house prices in some places by tying access to Chinese universities to local homeownership status in the presence of a rising college premium. We generate a novel dataset that combines China housing market and neighborhood data with household and university admission data. We find evidence of capitalization effects and a sizable increase in the likelihood of homeownership postpolicy change in places with the greatest preferential access to China's elite universities.     

Rational Expectations, Market Fundamentals and Housing Price Volatility

This paper derives a forward-looking rational expectations house price model and empirically tests its ability to explain short-run fluctuations in real house prices. A novel approach to proxying the imputed rents of owner-occupied housing, as a function of observable housing market fundamentals, is combined with a housing market arbitrage relation to derive a present value model for real house prices. Tests of the rational expectations, nonlinear cross-equation restrictions reject the joint null hypothesis of rational expectations and the asset-based housing price model for quarterly, single-detached house prices in the city of Vancouver, British Columbia from 1979–1991. The model fails to fully capture observed house price dynamics in two real estate booms but tracks real house prices well in less volatile times, suggesting that prices may temporarily deviate from fundamental values in real estate price cycles.     

Contact High: The External Effects of Retail Marijuana Establishments on House Prices

Using publicly available data from the city of Denver and the state of Colorado, this study examines the effects of retail conversions (conversions from medical marijuana to retail marijuana stores) on neighboring house values in Denver, CO. The study period reflects a time before and after retail marijuana sales became legal in Colorado in 2014. Using a difference‐in‐differences approach, we compare houses that were in close proximity to a conversion (within 0.1 miles) to those that are farther away from a conversion. We find that single‐family residences close to a retail conversion increased in value by approximately 8% relative to houses that are located slightly farther away. We perform a battery of robustness checks and falsification tests to provide additional support for this finding. To our knowledge, this is the first study to examine at a microlevel the highly localized effect of retail marijuana establishments on house prices and hope that it can contribute to the debate on retail marijuana laws.     

Border Effects in House Prices

This article estimates the effect of the Dutch–German border on house prices. We argue that the difference between house prices at the border indicates the willingness to pay to stay in a country compared to living across the border. After a change in the tax rules in 2001, migration from the Netherlands to Germany increased substantially and the gradient of Dutch house price towards the German border steepened. Combining a German and Dutch real estate dataset and using different estimation strategies, we find that asking prices of comparable housing drop by about 16% when one crosses the Dutch–German border.     

The Accuracy of Owner-Provided House Values: The 1978–1991 American Housing Survey

The American Housing Survey (AHS) includes the owner's valuation of the house as a measure of the house's value. If owner-stated values are accurate, the AHS (as well as other survey instruments) can be used by researchers studying a variety of topics. In this study we use the metropolitan version of the AHS for three cities over fourteen years to compare owners' valuations with sales prices of houses that sold in the twelve months prior to an interview. We find that, on average, recent buyers report house values that are 8.4% higher than the stated sales prices. Further analysis indicates that these recent buyers, when compared with owners with longer tenure, overvalue their houses by 3.3%, on average. Thus, we find that the average owner overvalues his house by 5.1%. Also, differences between sales prices and owners' valuations are not related to particular characteristics of the house, occupants (other than length of tenure), or neighborhood. Thus, the use of the owners' valuations will result in accurate estimates of house price indexes and will provide reliable estimates of the prices of house and neighborhood characteristics.     

基于ESDA方法与空间计量模型的房价溢出效应分析

一个地区房价的波动可以通过时间滞后传递到其他地区,从而产生房价溢出效应。本文以我国31个省际区域2005～2014年的数据为样本,运用探索性空间数据分析（ESDA）对31个省的房价空间分布格局及演变态势进行分析。然后运用空间计量模型分析我国省际房价变动的影响因素,并从地理因素和经济因素两个方面研究房价溢出效应。研究结果表明：中国31个省域间的房价存在明显的空间相关性;地区的城市化率、城镇就业人员平均货币工资、二、三产业产值占GDP比重对房价有显著的影响,房价存在显著的空间溢出效应,经济距离对地区房价影响比地理距离的影响更显著。     

The Measurement and Determinants of Single-Family House Prices

We assess the conceptual and empirical features of a number of house price series for the United States. We then calculate a measure of the net up-grading of the existing stock of houses that took place during the 1950–1989 period and adjust price indexes for this net increase in quality. Judgments about the trend, volatility, and determinants of house prices are shown to depend crucially on which price series is used. The Freddie Mac upgrade-adjusted house price measure rose 5.7% over the past four decades, falling 7.7% from 1950 through 1970 before rising 14.5% from 1970 through 1989. Real house prices declined in the early 1980s due to the increase in real after-tax interest rates and the decline in real materials costs. The recovery of house prices in the late 1980s is attributed to lower unemployment and real after-tax interest rates and particularly to demographic factors associated with the aging of baby boomers.     

Housing Attributes Associated with Capital Gain

Housing units are heterogeneous goods. Rates of change in housing prices are typically modelled as if they arise from factors unrelated to the housing unit itself. For example, housing price increases in the latter part of the 1970s and early 1980s are argued to have arisen primarily from demographic factors and the differential effects of inflation on the effective rate of taxation on income from corporate capital and on owner-occupied housing. Cross-sectional variation in price inflation is not addressed. Consumers who purchased housing units are not indifferent to their attributes. To the extent that expectations vary within regional housing markets as a consequence of variation in housing attributes, standard linear hedonic price regression may generate biased estimates of implicit prices. This paper identifies sufficient conditions for the estimates of implicit prices in linear hedonic price regressions to be unbiased and generate unbiased estimates of implied price changes. Finally, this paper identifies living space (house size) as a significant attribute related positively to the increase in individual housing prices in a regional market.     

Inequalities of opportunity in the access and use of telecommunication services in Togo

The liberalization of the telecommunications sector in Togo aimed at ensuring access to a better quality of telecommunication services at affordable prices to everyone. While the liberalization policy has increased competition in the sector and reduced significantly telecommunication services prices charged to costumers, statistics on the access and use of telecommunication services in Togo reveal disparities with respect to individuals' socio-economic characteristics which could be an indication of the existence of inequalities of opportunities in the access and use of these services.This paper investigates the inequalities of opportunity in the access and use of telecommunication services in Togo using a parametric approach. The results of estimations indicate that, inequalities of opportunities are larger for men and the elderly than respectively women and youths and are more pronounced in urban areas and regions such as the Plateaux and Savanna regions. Moreover, the circumstance variables such as the ‘place of residence’ and the ‘region’ are the key variables in explaining the inequalities of opportunities in the access and use of telecommunication services in Togo. So, policies toward reducing inequalities of opportunity in the access and use of telecommunication services in Togo should be designed with regards to men, the elderly, and people living in urban areas and regions such as the Plateaux and Savanna regions.     

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 Suburban Housing Market: Effects of City and Suburban Employment Growth

Using a data set of over 88,000 housing sales, we find that city employment growth has a significant positive effect on suburban house values; this effect is largest for housing closest to the central business district and declines with increasing distance from it. City employment growth has a negative effect on the rate of suburban house construction; the magnitude of the effect increases with distance. Suburban employment growth has little aggregate effect on house prices, and there is less variation by distance. Suburban growth has a significant effect on construction rates, especially at locations near the urban fringe.     

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.