A spatial semiparametric M-quantile regression for hedonic price modelling (original) (raw)
Related papers
Modelling housing prices in Istanbul applying the spatial quantile regression
This study examines, based on the hedonic approach, the factors that affect the house prices in Istanbul. The hedonic price models are estimated with quantile regression and spatial dependence in different conditional distribution of housing prices responds differently to residential properties at different quantiles is taken into account. According to the results obtained especially in middle and high quantiles, spatial dependence is observed to be significant.
Market heterogeneity and the determinants of Paris apartment prices: A quantile regression approach
Urban Studies, 2016
In this paper, the heterogeneity of the Paris apartment market is addressed. For this purpose, quantile regression is applied – with market segmentation based on price deciles – and the hedonic price of housing attributes is computed for various price segments of the market. The approach is applied to a major data set managed by the Paris region notary office (Chambre des Notaires d’Île de France), which consists of approximately 156,000 transactions over the 2000–2006 period. Although spatial econometric methods could not be applied owing to the unavailability of geocodes, spatial dependence effects are shown to be adequately accounted for through an array of 80 location dummy variables. The findings suggest that the relative hedonic prices of several housing attributes differ significantly among deciles. In particular, the elasticity coefficient of the apartment size variable, which is 1.09 for the cheapest units, is down to 1.03 for the most expensive ones. The unit floor level, ...
Regional Science and Urban Economics, 2016
In housing price regression, a large bundle of non-separable structural and location characteristics, potentially affecting prices nonlinearly, constitute the relevant set of predictors. Spatial subcenters and complex spatial association structures may, therefore, exist or, stated differently, horizontal market segmentation might be prevalent. Moreover, it is not unlikely for the housing price generating market mechanisms to vary across different parts of the conditional price distribution. This can ultimately cause disparate price segments to exhibit varying functional relationships through different subsets of characteristics and lead to vertical market segmentation. In order to take nonlinearity, horizontal and vertical market segmentation into account within the scope of housing price regressions, we propose incorporating a semiparametric approach into the quantile regression framework. In our empirical application, we investigate rental data from the German city of Regensburg, which contains an Old Town on the World Heritage List. Focusing on location effects exerted by the World Heritage Site, we illustrate how statements about horizontal and vertical market segmentation can be derived from a semiparametric quantile regression model based on empirical evidence and economic reasoning.
Heterogeneity and the Determinants of Paris Apartment Prices : A Quantile Regression Approach
2013
In this paper, the heterogeneity of the Paris apartment market is addressed through assessing the differences in the hedonic price of housing attributes over the 2000-2006 period for various price, hence income, segments of the housing market. For that purpose, quantile regression is applied to the 20 Paris “arrondissements” as well as to the 80 neighborhoods, called “quartiers” – or quarters (each “arrondissement” is composed of four quarters), with market segmentation being based on price deciles (deciles 1 to 9). The database includes some 159,000 sales spread over a seven year period (2000 – 2006). Housing descriptors include, among other things, a price index, building age, apartment size, number of rooms and bathrooms, unit floor level, the presence of a lift and of a garage, the type of street and access to building (boulevard, square, alley, etc.) as well as a series of location dummy variables standing for both the “arrondissements” and the quarters. Findings clearly sugges...
Determinants of House Prices: A Quantile Regression Approach
The Journal of Real Estate Finance and Economics, 2008
OLS regression has typically been used in housing research to determine the relationship of a particular housing characteristic with selling price. Results differ across studies, not only in terms of size of OLS coefficients and statistical significance, but sometimes in direction of effect. This study suggests that some of the observed variation in the estimated prices of housing characteristics may reflect the fact that characteristics are not priced the same across a given distribution of house prices. To examine this issue, this study uses quantile regression, with and without accounting for spatial autocorrecation, to identify the coefficients of a large set of diverse variables across different quantiles. The results show that purchasers of higher-priced homes value certain housing characteristics such as square footage and the number of bathrooms differently from buyers of lower-priced homes. Other variables such as age are also shown to vary across the distribution of house prices.
Sustainability
After almost a decade of crisis, the housing market in Spain shows significant signs of recovery, with increases in both the average price and the number of sales transactions. Housing is the main asset for the majority of households, and it also has the most resources devoted to it, thus, when it comes to buying a residence, people do not only look at the asset’s intrinsic characteristics, but also consider other particularities such as the neighbourhood, accessibility to services, availability of public transport or adequate funding. The study aimed to analyse and quantify the relationship that exists between the asking price of second-hand housing on the market in Alicante and the attributes that characterise them. This was done using a multivariate analysis to estimate a hedonic pricing model by ordinary least squares and a quantile regression to analyse the impact of the characteristics in different price ranges. The results show the segmentation of the prices in the Alicante m...
Quantile Regression Estimates of Hong Kong Real Estate Prices
Urban Studies, 2010
Linear regression is a statistical tool used to model the relation between a set of housing characteristics and real estate prices. It estimates the mean value of the response variable, given levels of the predictor variables. The quantile regression approach complements the least squares by identifying how differently real estate prices respond to a change in one unit of housing characteristic at different quantiles, rather than estimating the constant regression coefficient representing the change in the response variable produced by a one-unit change in the predictor variable associated with that coefficient. It estimates the implicit price for each characteristic across the distribution of prices and allows buyers of higher-priced properties to behave differently from buyers of lowerpriced properties, even if they are within one single housing estate. Thus, it provides a better explanation of the real-world phenomenon and offers a more comprehensive picture of the relationship between housing characteristics and prices.
Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna
The Journal of Real Estate Finance and Economics, 2009
We apply additive mixed regression models (AMM) to estimate hedonic price equations. Non-linear effects of continuous covariates as well as a smooth time trend are modeled non-parametrically through P-splines. Unobserved district-specific heterogeneity is modeled in two ways: First, by location specific intercepts with the postal code serving as a location variable. Second, in order to permit spatial variation in the nonlinear price gradients, we introduce multiplicative scaling factors for nonlinear covariates. This allows highly nonlinear implicit price functions to vary within a regularized framework, accounting for district-specific spatial heterogeneity. Using this model extension, we find substantial spatial variation in house price gradients, leading to a considerable improvement of model quality and predictive power.
House prices and neighbourhood amenities: beyond the norm?
International Journal of Housing Markets and Analysis, 2018
Purpose Understanding the key locational and neighbourhood determinants and their accessibility is a topic of great interest to policymakers, planners and property valuers. In Northern Ireland, the high level of market segregation means that it is problematic to understand the nature of the relationship between house prices and the accessibility to services and prominent neighbourhood landmarks and amenities. Therefore, this paper aims to quantify and measure the (dis)amenity effects on house pricing levels within particular geographic housing sub-markets. Design/methodology/approach Most hedonic models are estimated using regression techniques which produce one coefficient for the entirety of the pricing distribution, culminating in a single marginal implicit price. This paper uses a quantile regression (QR) approach that provides a “more complete” depiction of the marginal impacts for different quantiles of the price distribution using sales data obtained from 3,780 house sales tr...