Housing Price Gradient Research Articles

Does the renovation of waste-to-energy incineration plants attenuate the housing price gradient? Evidence from a quasi-natural experiment in Shenzhen, China

Waste-to-energy (WTE) incineration plants, which inevitably cause negative externalities due to their impacts on the surrounding environment, have encountered frequent protests from residents during their construction and operation. The negative externalities could lead to property value losses by impacting residents' marginal willingness to pay (MWTP). To assess whether the renovation of WTE incineration plants can attenuate the housing price gradient around the plants, we use the completion of the renovation project of Yantian Plant in Shenzhen as a quasi-natural experiment to exploit difference-in-differences (DID) estimators, and propose a hedonic price model based on resale apartment transaction data within 10 km of the plant. Results indicate that the renovation completion narrows the price gap between proximate and distant apartments from the plant by 35.25%, and the housing price gradient begins to decrease significantly in the late construction period. Moreover, the effect on the housing price gradient remains sustained throughout the whole post-treatment period. Furthermore, the analysis of the underlying mechanism suggests that the renovation completion attenuates the housing price gradient through the improved environment.

JUE Insight: COVID-19 and household preference for urban density in China

This paper investigates the effect of COVID-19 on both housing prices and housing price gradients in China using transaction level data from 60 Chinese cities. After using a difference-in-differences (DID) specification to disentangle the confounding effects of China's annual Spring Festival, we find that housing prices decreased by two percent immediately after the COVID-19 outbreak but gradually recovered by September 2020. Moreover, our findings suggest that COVID-19 flattens the horizontal housing price gradient, reduces the price premium for living in tall buildings, and changes the vertical gradient within residential buildings. This is likely explained by the changing household preferences towards low-density areas associated with lower infection risk.

Vertical Housing Price Gradient and Ground Floor Premium in Taipei City

This study aims to examine the influence of the floor level on housing prices based on the vertical dimensions of the building. A two stage least squares regression (2SLS) and two stage quantile regression (2SQR) are utilized to analyze the urban regions in Taipei City by using data obtained from the online actual price registry of real estate transactions from August 2012 to April 2020. After considering the spatial autocorrelation, the following conclusions are reached. First, in addition to the ground floor, the floor level premium is 0.55% for residential buildings with an elevator, and the premium increases when the housing price quantile increases. However, for walk-up apartments, there is a floor level discount of -4.21%, and the price discount increases when the quantile for the housing price increases. Secondly, when compared to the other floors, the ground floor premium is 9.61% for residential buildings with an elevator, which is a little higher than walk-up apartments for which the ground floor premium is 8.54%. The results reveal that the influence of the type of residential building and the total number of floors on the ground floor premium is not as high as expected. Finally, regardless of whether the residential buildings have an elevator or are walk-up apartments, the ground floor premium increases when the housing price quantile increases. The ground floor premiums for residential buildings with an elevator range from 7.30% to 11.85%. They are a little higher than those for walk-up apartments which range from 6.25% to 9.69%.

Housing Dynamics: Theory Behind Empirics

We construct a dynamic general equilibrium model of housing, incorporating some key features that bridge time and space. We model explicitly the evolution of housing structures/household durables and the separate role played by land, fully accounting for households’ locational choice decisions. Housing services derive positive utility, but are decayed away from the city center. Our model enables a full characterization of the dynamic paths of housing as well as housing and land prices. The model is particularly designed to be calibrated to fit some important stylized facts, including faster growth of housing structure/household durables than housing, faster growth of land prices than housing prices, a locationally steeper land rent gradient than the housing price gradient, and relatively flatter housing quantity and price gradients in larger cities with flatter population gradients. The calibrated model is then used to quantitatively assess the dynamic and spatial consequences of demand and supply shifts. We find that nonhomotheticity in forms of income-elastic spending on housing/household durables and minimum structure requirement in housing production are essential ingredients.

The House Price Gradient: Evidence from Italian Cities

The paper examines the geographical variability in house prices, focusing on the gradient from the center to peripheral areas. Our results show large price differentials within the main urban areas, even larger than those observed between the Center-North and South of Italy. The higher real estate prices in the centers of urban areas are affected by centripetal pressures from the demand side, to which supply only partially adjusts. We find that better infrastructure and lower commuting times can, however, mitigate the centripetal pressure on the demand side and flatten the gradient. Finally, we also find a center-periphery gradient for incomes ; however the latter is less steep than that of house prices, thus indicating that the ratio between the value of houses and income is higher in the city center and lower in the periphery.

Oil Prices and Urban Housing Demand

AbstractWe 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.

The impact of the cost of car ownership on the house price gradient in Singapore

This paper examines the extent to which a change in the cost of car ownership affects the house price gradient with respect to distance from the central business district (CBD). Theory suggests that if the cost of owning a car increases, people will use other modes of transportation, thus reducing house prices farther away from the CBD and increasing house prices closer to the city center. However, in an empirical setting, the cost of car ownership is likely to be endogenous and correlated with various unobserved factors that also contribute to changes in the house price gradient. To obtain causal effects, we exploit a unique feature of Singapore's car registration process. All cars in Singapore must have a Certificate of Entitlement (COE), but the number available is restricted based on the traffic concerns of the government and are allocated through a competitive bidding process. We use the number of COEs available each quarter as an instrument for the price of a COE, as the quota is likely to be correlated with the price of the COE but not the price of housing at various distances from the CBD. We find that when the price of a COE increases, the price of housing closer to the city center increases, suggesting that increases in the price of a car cause individuals to increase their willingness to pay to locate closer to the CBD.

Immigrants and the City: The Relevance of Immigration on Housing Price Gradient

Foreign citizens are a more and more significant part of the population of Italian cities and society (8% of the country’s total population), and they contribute to changes in the cultural, social, and economic structure of the country. Our aim was to assess the incidence of the immigrant population on urban house price polarization, as measured using an original indicator: the center-periphery housing price gradient. While there is ample literature on the relationship between average prices and immigrant populations, less research has been conducted on immigration and the housing price gradient on a national scale. This price gradient may indicate whether immigration contributes to changing the residential market, also possibly revealing segregation phenomena. We ran multivariate regressions in several steps on an original dataset of housing prices and socio-economic factors concerning 112 Italian provincial capitals to elucidate whether immigration is correlated with the housing market divide. Our main findings confirmed that larger immigrant populations coincide with steeper housing price gradients on a national scale. Our tests also demonstrated that the relevance of this phenomenon varies for different urban forms, confirming related to housing price dynamics between the cities of northern and southern Italy the relevance of urban density in elucidating.

Spatial-difference-in-differences models for impact of new mass rapid transit line on private housing values

This study uses the opening of the new Circle Line (CCL) in Singapore as a natural experiment to test the effects of urban rail transit networks on non-landed private housing values. We use a network distance measure and a local-polynomial-regression approach to identify the CCL impact zone with discontinuity in housing price gradient between a treatment zone and a control zone. We then estimate the spatial difference-in-differences models that account for spatial autocorrelation in housing price changes in the two zones “before and after” the opening of the CCL. We find that the opening of the CCL increases housing value in the treated neighborhoods located within the 600-metre network distance from the new CCL stations by approximately 8.6%, relative to other properties in the untreated neighborhoods controlling for heterogeneities in housing attributes and local amenities, and spatial and temporal fixed effects. We find significant “anticipation” effects as early as 1 year prior to the opening of the CCL line, but the effects diminish closer to the actual opening date. The results imply that the inter-dependent spatial structure between the treated and the untreated neighborhoods, if neglected, may lead to over-estimation of the capitalization effects of the new transit lines on housing values.

Oil Prices and Urban Housing Demand

We develop a model of a monocentric, oil-exporting city. The model predicts producer price and transportation cost effects of oil price changes on the house price gradient. 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.

Land market, land development and urban spatial structure in Beijing

The paper first examines urban spatial patterns of the gradients of housing and land prices and land development intensity, and then tests the relationship between the land price gradient and housing price gradient. Urban theory predicts the former is steeper than the latter based on the notion of derived demand for land from the provision of housing services. Finally the paper examines the impact of the property of housing production function on urban spatial structure. For the property of housing production function, we are particularly interested in the elasticity of capital-land substitution. The paper concludes (1) market influences over spatial structure, (2) the derived demand for land, and (3) it is the actual (or expected) housing price increases that cause skyrocketing land prices, not the other way around.

Does comprehensive redevelopment change the housing price gradient? A case study in Mongkok, Hong Kong

Urban decay is an inevitable result of the growth of every city, and Hong Kong is not an exception. Many buildings were found to be in dilapidation, and there is an urgency to tackle urban decay in the city. Redeveloping dilapidated buildings has long been regarded as an effective solution to this urban problem. Yet, as suggested in the literature, redevelopment may be liable for gentrifying the neighbourhood because it pushes upon the property prices and rentals in the vicinity of redeveloped sites. However, empirical studies on how comprehensive redevelopment affects housing values in the neighourhood are not common. In this light, this research investigates the impacts of Argyle Street/Shanghai Street Redevelopment Project undertaken by the Urban Renewal Authority on the sale prices of housing nearby. A panel data is employed and the change in the spatial price gradient before and after the redevelopment project is explored. The analysis results suggest that proximity to the project site had a significant positive impact with housing prices before the project. Yet, no change in the spatial-price gradient is spotted after the completion of the project. These results confirm that housing prices do not response to the change in the environmental quality brought about by the comprehensive redevelopment.

The Effects of Urban Redevelopment on Neighbourhood Housing Prices

The topic of neighbourhood improvement is central to residential appraisal. The study empirically investigates how the redevelopment affects the housing values in the vicinity. Using price gradient approach based on hedonic pricing model, we study the effects of an urban redevelopment project on housing property prices during the pre-redevelopment stage, construction stage and post-redevelopment stage. The results show that there were significant increases in property prices between before implementation and after completion of the redevelopment project. Property value enhancements well ahead of the actual project completion are also observed. It suggests that redevelopment has brought overall improvements to the surrounding properties and the expectation of these advantages was taken into consideration in the pricing of the properties during the construction phase. In addition, the changes in housing price gradients reveal that the effects of redevelopment varied with proximity between the properties and the redeveloped site. Properties which have the closest proximity with the redevelopment are found to have experienced greater increase in price after redevelopment but less extent of price rise during construction. It is important for urban planners to carefully consider the externalities of redevelopment received by properties in different locations, in order to maximize the positive impacts of redevelopment on the neighbourhood.

Housing price gradient changes between Macau and Hong Kong: a neighboring city effect

PurposeThe classical land market model assumes a one‐workplace scenario. However, with the globalization trend, travel between two workplaces is becoming more and more common. This paper therefore aims to study the housing price gradient changes between Macau and Hong Kong to test the two workplaces hypotheses.Design/methodology/approachThe temporal‐spatial difference of housing prices between the two neighboring cities are studied by using a two‐workplace residential location choice model.FindingsThis paper found that housing price gradient from Macau to Hong Kong is flattened when more non‐resident workers traveled from Hong Kong to Macau (ceteris paribus).Originality/valueThe results have important implications for polycentric city models and provide a novel method to study neighboring city effects on housing price.

Housing Price Gradients in a Region with One Dominating Center

This paper primarily focuses on predicting housing price gradients in a Norwegian region with one dominating center. Spatial separation is represented by a function of the traveling distance from the city center in a traditional hedonic regression equation. Several functions are tested, and some alternatives provide a satisfying goodness-of-fit, consistent coefficient estimates, and intuitively reasonable predictions of housing price gradients. Still, not all commonly used functions are recommended. The findings also indicate that the strength of spatial autocorrelation is reduced when the hedonic function is properly specified.

Housing price gradient with two workplaces — An empirical study in Hong Kong

The classical land market model assumes a one-workplace scenario. However, with the globalization trend, travel between two workplaces is becoming more and more common. This paper develops a residential location choice model based on the classical land market model, but relaxes the assumption of a single workplace. The unit housing price is shown to be higher for housing which is closer to the more frequently traveled workplace, and the price gradient is also shown to have changed, with respect to the relative visiting frequencies of the workplaces. Empirical evidence is given by studying housing prices along a major railway in Hong Kong. The results have important implications on the predictive power of polycentric models.

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.

Residential property values in a multinodal urban area: New evidence on the implicit price of location

The monocentric model predicts a housing price gradient from the central business district, and it follows that the extension of this model to account for modern multinodal metropolitan areas would predict housing price gradients from multiple employment centers. Empirical analysis using hedonic regression techniques for the estimation of price gradients in a multinodal context is limited. This study extends prior work by exploring nonlinear housing price gradients in a multinodal urban area with an unusually robust database of housing sales transactions, and using a geographic information system for spatial analysis. The results confirm the importance of non-CBD employment centers, a strong if asymmetric CBD price gradient, and significant nonlinear gradients from such other urban amenities as major retail sites and highways.

Housing Price Gradients: The Intersection of Space and Built Form

A hedonic housing price model, implemented for the Dallas region, reveals a housing market structured around multiple nodes, some of which give rise to positive and others to negative externalities. The utility/disutility derived from relative location is capitalized into the size and quality of the housing stock and the nature of neighborhood amenities. The result is a convergence of space and built form.

TESTS OF THE STANDARD URBAN MODEL: A Micro (Trade-Off) Alternative

Three methods of testing the validity of the monocentric model (the negative house price gradient, the wasteful commuting approach, and the hypothesis of a trade-off between housing expenditures and commuting costs) are evaluated. The negative house price gradient is not supported by several recent hedonic models. “Wasteful” commuting is a poor test because it persists even a policentric world and may be more the result of the weakness of journey-to-work minimization as a key determinant of residence and/or workplace location. This study uses the commuting information presented in the 1985 American Housing Survey in eight large metropolitan areas to show that the trade-off hypothesis fails badly. Heterogeneous Preference for housing, commuting and indeed for all goods and services, combined with a wide variety of workplaces and choice of residential locations at highly, variable rents and house prices may be the major reason why the trade-off is not generally observed.