Hedonic House Price Research Articles

주택 거래 간 시공간 의존성을 고려한 헤도닉 가격 추정

주택 거래 간 시공간 의존성을 고려한 헤도닉 가격 추정

Accounting for spatial variation of land prices in hedonic imputation house price indexes

Location is capitalized into the price of the land the structure of a property is built on, and land prices can be expected to vary significantly across space. We account for spatial variation of land prices in hedonic house price models using geospatial data and a semi-parametric method known as mixed geographically weighted regression. To measure the impact on aggregate price change, quality-adjusted (hedonic imputation) house price indexes are constructed for a small city in the Netherlands and compared to price indexes based on more restrictive models, using postcode dummy variables or no location information at all. We find that, although taking spatial variation of land prices into account improves the model performance, the Fisher house price indexes based on the different hedonic models are almost identical. The land and structures price indexes, on the other hand, are sensitive to the treatment of location.

A New Approach to Estimating Equilibrium Models for Metropolitan Housing Markets

We formulate and estimate a new equilibrium model of metropolitan housing markets with housing differentiated by quality. Quality is a latent variable that captures all features of a dwelling and its environment. We estimate the model for Chicago and New York, obtaining hedonic housing price functions for each quality level for each metropolitan area, stocks of each quality, and compensating variations required for a household of a given income in Chicago to be equally well off in New York.

Housing Market Hedonic Price Study Based on Boosting Regression Tree

Based on the purchase price data of new real estate markets three cities in China, Beijing, Shanghai, and Guangzhou, including architectural features, neighborhood property features, and location features, in this study a boosting regression tree model was built to study the factors and the influence path of housing prices from the microcosmic perspective. First, a classical hedonic price model was constructed to analyze and compare the significant effect factors on housing prices in the market segments of the three cities. Second, the gradient boosting regression tree method that is proposed in this paper was applied to the three markets in combination to analyze the influence paths and factors and the importance of the type of housing hedonic price. The influence paths of housing hedonic prices and decision tree rules are visualized. The significant housing features are effectively extracted. Finally, we present three main conclusions and several suggestions for policy makers to improve urban functions while stabilizing real estate prices.

Can Geospatial Data Improve House Price Indexes? A Hedonic Imputation Approach with Splines

Determining how and when to use geospatial data (i.e. longitudes and latitudes for each house) is probably the most pressing open question in the house price index literature. This issue is particularly timely for national statistical institutes (NSIs) in the European Union, which are now required by Eurostat to produce official house price indexes. Our solution combines the hedonic imputation method with a flexible hedonic model that captures geospatial data using a non‐parametric spline surface. For Sydney, Australia, we find that the extra precision provided by geospatial data as compared with postcode dummies has only a marginal impact on the resulting hedonic price index. This is good news for resource‐stretched NSIs. At least for Sydney, postcodes seem to be sufficient to control for locational effects in a hedonic house price index.

Construction of real estate price indices for developing housing markets

PurposeThe purpose of this paper is to test whether temporal aggregation matters when constructing hedonic house price indices for developing markets using Ghana as a case study.Design/methodology/approachMonthly, quarterly, semi-yearly and yearly hedonic price indices are constructed and six null hypotheses are tested using the F-ratios to examine the temporal aggregation effect.FindingsThe results show that temporal aggregation may not be a serious issue when constructing hedonic house price indices for developing markets as a result of the smaller sample size which these markets normally have. At even 10 per cent significance level, none of the F-ratios estimated is statistically significant. Analysis of the mean returns and volatilities reveal that indices constructed at the lower level of temporal aggregation are very volatile, suggesting that the volume of transactions can affect the level of temporal aggregation, and so, the temporal aggregation level should not be generalised, as is currently observed in the literature.Originality/valueThe diversification importance of real estate and the introduction of real estate derivatives and home equity insurance as financial products call for the construction of robust and accurate real estate indices in all markets. While almost all empirical research recommends real estate price indices to be conducted at the lower level of temporal aggregation, these studies are largely conducted in developed markets where transactions take place frequently and large transaction databases exist. Unfortunately, little is known about the importance of temporal aggregation effect when constructing indices for developing real estate markets. This paper contributes to fill these gaps.

Searching for Isard’s Regional Essence

At the inception of the Southern Regional Science Association in the early 1960s, Walter Isard maintained that areal regions are fundamental units of observation and analysis. To this day, understanding the special significance of regions motivates much of our empirical research. In my address, I argue that regional fixed effects estimates from regression analysis can help us comprehend the distinctive character of areal units. This paper offers two examples from my research. First, I present the results of a regression model that explains regional knowledge spillovers in U.S. counties. Santa Clara County (Silicon Valley) has by far the largest fixed effect estimate of any U.S. county. Overall, the county level fixed effects for knowledge spillovers follow a pattern of rapid exponential decay. Next, I inspect neighborhood fixed effects taken from a hedonic housing price model of the Charleston, South Carolina region. The results suggest a clear preference for coastal proximity as reflected in house prices. For neighborhoods away from the coast, the fixed effects estimates exhibit a steep decline toward zero. Like Silicon Valley’s regional advantage in knowledge, beachfront neighborhoods benefit from an exclusive, time invariant advantage that is hard, if not impossible, to replicate in space.

A model to Estimate the Implicit Values of Housing Attributes by Applying the Hedonic Pricing Method

Many scholars focused on the location based attributes rather than the non-location factors in decision making on land prices. Further, new research studies have identified the importance of the non-location attributes with the location factors. Many studies suggest that, many attributes exist which affects the housing price. Since the attributes involved and dominant for a particular case differs from one situation to the other, there cannot be an exact list of attributes. Yet, identification of factors that determine housing price and their relationships and the level of influence have poorly understood in planning and property development in the context of Sri Lanka. This study attempts to address what make householders to decide on housing price and application of hedonic pricing approach to estimate the implicit price of housing attributes in context of Sri Lanka. A sample study of selected fifty (50) single house transactions in Maharagama urban neighborhood area has been utilized to illustrate the applicability of the hedonic pricing model. As a methodology, correlation analysis has been carried out to study the degree of relationship between the housing price and the independent variables. The attributes which correlate with housing prices, the study identified the most significant attributes. A model was developed to estimate the future house price by applying the pricing model which is incorporated with these attributes. A hedonic house price model derived from multiple liner regression analysis was developed for the purpose. The findings reveal that six attributes as design type of the house, distance to the local road, quality of Infrastructure, garden size, number of the bed rooms and property age are contributed to estimate the implicit value of Housing property. The model developed would be used to identify implicit values of houses located in urban neighborhood area of Sri Lanka.

A two-step approach to account for unobserved spatial heterogeneity

ABSTRACTA two-step approach to account for unobserved spatial heterogeneity. Spatial Economic Analysis. Empirical analysis in economics often faces the difficulty that the data are correlated and heterogeneous in some unknown form. Spatial econometric models have been widely used to account for dependence structures, but the problem of directly dealing with unobserved spatial heterogeneity has been largely unexplored. The problem can be serious particularly if we have no prior information justified by economic theory. In this paper we propose a two-step procedure to identify endogenously spatial regimes in the first step and to account for spatial dependence in the second step. This procedure is applied to hedonic house price analysis.

Functional regression over irregular domains: variation in the shadow price of living space

ABSTRACTFunctional regression over irregular domains: variation in the shadow price of living space. Spatial Economic Analysis. Hedonic house price models need to account for spatial heterogeneity – the variation in the functional surface of shadow prices. In this context, the complexity of spatial domains raises issues for traditional spatial econometric methods. Specifically, discontinuities in the spatial surface need to be accounted for, including irregular boundaries, peninsulas and interior holes. Motivated by an application to housing markets, we develop a method for estimating the functional surface of a regression coefficient that varies over such an irregular spatial domain. Spatially varying coefficients for a specific regressor are estimated by a combination of three smoothing problems using splines based on finite element analysis. The effect of additional regressors is also allowed. We verify finite sample performance using a simulation study, and develop an application to the Aveiro–Ílhavo urban housing market in Portugal.

Valuing the Redevelopment Option Component of Urban Land Values

AbstractThis study draws from the redevelopment, real option, and urban spatial growth literatures to explore the spatial dynamics of the components of house prices. More specifically, the paper proposes that the capitalized value of the option to redevelop housing at the property level can be estimated by incorporating the likelihood of exercising the redevelopment option (the probability of redevelopment) into spatial and nonspatial hedonic house price models. Accordingly, option values are estimated for properties across the spectrum of the housing life cycle. Results from the study reveal a substantial level of spatial variation and clustering in the predicted option values, indicating that location is a major determinant of redevelopment and real option values. Furthermore, the results provide new evidence in support of the theoretical construct that properties purchased for immediate redevelopment are only valued for the underlying land.

The Value-Undermining Effects of Rock Mining on Nearby Residential Property: A Semiparametric Spatial Quantile Autoregression

Rock mining operations, including limestone and gravel production, have considerable adverse effects on residential quality of life due to elevated noise and dust levels resulting from dynamite blasting and increased truck traffic. This paper provides the first estimates of the effects of rock mining — an environmental disamenity — on local residential property values. We focus on the relationship between a house’s price and its distance from nearby rock mine. Our analysis studies Delaware County, Ohio which, given its unique features, provides a natural environment for the valuation of property-value-suppressing effects of rock mines on nearby houses. We improve upon the conventional approach to valuating adverse effects of environmental disamenities based on hedonic house price functions. Specifically, in a pursuit of robust estimates, we develop a novel (semiparametric) partially linear spatial quantile autoregressive model which accommodates unspecified nonlinearities, distributional heterogeneity as well as spatial dependence in the data. We derive the consistency and normality limit results for our estimator as well as propose a consistent model specification test. We find statistically and economically significant propertyvalue-suppressing effects of being located near an operational rock mine which gradually decline to insignificant near-zero values at a roughly ten-mile distance. Our estimates suggest that, all else equal, a house located a mile closer to a rock mine is priced, on average, at about 2.3–5.1% discount, with more expensive properties being subject to larger markdowns.

The Spatial Dimension of House Prices

The Spatial Dimension of House Prices

The Spatial Dimension of House Prices

The Spatial Dimension of House Prices

Housing affordability in Australia: an empirical study of the impact of infrastructure charges

Developer paid infrastructure charges are a commonly used mechanism for Australian municipalities to shift the funding of new urban infrastructure to private sector property developers. In a climate where housing affordability is a policy objective for many governments, a clear understanding of the impacts these charges have on the price of housing is imperative. This paper empirically examines the effect of infrastructure charges on housing affordability in Brisbane, Australia, applying a hedonic house price model to 4699 new and 25,053 existing house sales in Brisbane from 2005 to 2011. The findings of this research are consistent with international studies that support the proposition that infrastructure charges are over passed to home buyers. This study provides evidence of infrastructure charges being over passed to both new and existing home buyers in the order of around 400 %. These findings suggest that infrastructure charges are thus a significant contributor to increasing house prices and reduced housing affordability in Australia. By testing this flow-on effect to both new and existing homes, this research provides evidence in support of the proposition that not only are infrastructure charges over passed to new home buyers, but also to buyers of existing homes. Thus the price inflationary effect of infrastructure charges are being felt by all home buyers across the community, resulting in increased mortgage repayments of close to $1000 per month in Australia. These results are important as they inform governments on the outcomes of current infrastructure funding policies on housing affordability, providing the first empirical evidence of its kind in Australia.

A hedonic house price index in continuous time

Purpose This paper aims to develop a methodology to accurately and timely measure movements in housing markets by constructing a continuously estimated house price index. Design/methodology/approach The continuous index, which is extracted from an additive model that includes the temporal and the locational effects as smooth functions, can be interpreted as an extension of the classical hedonic time-dummy method. The methodology is applied to housing sales from Sydney, Australia, between 2001 and 2011, and compared to three types of discrete indexes. Findings Discrete indexes turn out to approach the continuously estimated index with decreasing period lengths but eventually become wiggly and unreliable because of fewer observations per period. The continuous index, in contrast, is stable, has favourable robustness properties and is more objective in several ways. Originality/value The resulting index tracks movements in the housing market precisely and in “real-time” and is hence suited for monitoring and assessing housing markets. Because turbulence in housing markets is often a harbinger of financial crises, such monitoring tools support policymakers and investors in tailoring their decisions and reactions. Additionally, the index can be evaluated arbitrarily frequently and therefore is well suited for use in property derivatives.

Conform or Resist? Spatio-Temporal Spillover Effects of Surrounding Apartment Transactions in a Boom and a Recession

As the transaction price of a comparable property in the local housing market is one the most accessible references for sellers and buyers, a change in a house price may result in spillover effects on future transactions of neighbouring properties. Given the dynamics on the real estate market, we can observe asymmetric effects, with those spillover effects being more pronounced in a rising market rather than in a falling market. This paper examines this hypothesis by comparing the spatio-temporal dependence in house prices during a boom and a recession period. Using data on 30,541 apartment transactions from Seoul, South Korea, this study finds that neighbouring property values are eight times more likely to spill over onto future transactions in a rising housing market as opposed to a falling one. We apply a spatio-temporal autoregressive model, rather than a spatial model, to control for spurious spatial relations that future transactions exert influence on past transitions. We show that accounting for unidirectional temporal relations improves the application of spatial econometrics in a hedonic house price model.

A varying coefficient approach to estimating hedonic housing price functions and their quantiles

ABSTRACTThe varying coefficient (VC) model introduced by Hastie and Tibshirani [26] is arguably one of the most remarkable recent developments in nonparametric regression theory. The VC model is an extension of the ordinary regression model where the coefficients are allowed to vary as smooth functions of an effect modifier possibly different from the regressors. The VC model reduces the modelling bias with its unique structure while also avoiding the ‘curse of dimensionality’ problem. While the VC model has been applied widely in a variety of disciplines, its application in economics has been minimal. The central goal of this paper is to apply VC modelling to the estimation of a hedonic house price function using data from Hong Kong, one of the world's most buoyant real estate markets. We demonstrate the advantages of the VC approach over traditional parametric and semi-parametric regressions in the face of a large number of regressors. We further combine VC modelling with quantile regression to examine the heterogeneity of the marginal effects of attributes across the distribution of housing prices.

The Hedonic Housing Price Index For Poland Modelling on NBP BaRN Data

The literature abounds with arguments regarding the relevance of reliable measurement of home prices. The correct measurement of home price growth is very important due to decisions taken by various economic agents and the impact of these decisions on the current situation in the domestic economy and its development. The construction of a reliable home price index is a challenging task mainly due to the nature of the housing market and housing itself and availability of relevant data sources concerning this market. The most important issues which have to be addressed by a researcher involved in the measurement of home price trends, are mainly connected with the following two features of a good called housing and the housing market itself: heterogeneous nature of a housing good (there are no two identical housing goods) and rare turnover in housing stock (that is, relatively small number of transactions as compared to the housing stock volume). This paper presents solutions to the abovementioned problems. Especially, the paper shows the construction of the hedonic house price index for Poland and describes in detail ts modeling on NBP BaRN data.

ACCOUNTING FOR LOCAL SPATIAL HETEROGENEITIES IN HOUSING MARKET STUDIES

ABSTRACTWe adopt a novel method to deal with omitted spatial heterogeneities in hedonic house price analysis. A Gaussian variant of the conditional autoregressive (CAR) model is used to study the impact of spatial effects. In a general linear modeling framework, we include zone‐specific random effects that are allowed to interact spatially with neighboring zones. The results demonstrate that this estimator accounts for missing spatial information, producing more reliable results on estimated spatially related coefficients. The CAR model is benchmarked against a fixed effects model. Socioeconomic neighborhood characteristics are found to have only modest impact on spatial variation in housing prices.