Semi-Parametric Hedonic Models, and Empirical Comparison

Hedonic models have been widely used in the literature for valuation of non- market goods such as air quality. While the inclusion of air quality variables in hedonic models is common in applied work, there is not theoretical basis for defining the functional relation between air quality and house prices. Estimation of semiparametric models that allow the data to determine the functional form may provide more insight on the real relationships suggested by the data rather than imposing the constraints of fully parametric models. Using an instrumental variable estimator, I explore the advantages of using semiparametric models in the estimation of spatial hedonic models by comparing the economic estimates from a parametric spatial lag model with those of a semiparametric specification, where the environmental variable is introduced nonparametrically.air quality valuation, endogeneity, hedonic models, real estate markets., semi-parametric, spatial econometrics

A Spatial Quantile Regression Hedonic Model of Agricultural Land Prices

Abstract Land price studies typically employ hedonic analysis to identify the impact of land characteristics on price. Owing to the spatial fixity of land, however, the question of possible spatial dependence in agricultural land prices arises. The presence of spatial dependence in agricultural land prices can have serious consequences for the hedonic model analysis. Ignoring spatial autocorrelation can lead to biased estimates in land price hedonic models. We propose using a flexible quantile regression-based estimation of the spatial lag hedonic model allowing for varying effects of the characteristics and, more importantly, varying degrees of spatial autocorrelation. In applying this approach to a sample of agricultural land sales in Northern Ireland we find that the market effectively consists of two relatively separate segments. The larger of these two segments conforms to the conventional hedonic model with no spatial lag dependence, while the smaller, much thinner market segment exhibits considerable spatial lag dependence. Un mod�le h�donique � r�gression quantile spatiale des prix des terrains agricoles R�sum� Les �tudes sur le prix des terrains font g�n�ralement usage d'une analyse h�donique pour identifier l'impact des caract�ristiques des terrains sur le prix. Toutefois, du fait de la fixit� spatiale des terrains, la question d'une �ventuelle d�pendance spatiale sur la valeur des terrains agricoles se pose. L'existence d'une d�pendance spatiale dans le prix des terrains agricoles peut avoir des cons�quences importantes sur l'analyse du mod�le h�donique. En ignorant cette corr�lation s�rielle, on s'expose au risque d'�valuations biais�es des mod�les h�doniques du prix des terrains. Nous proposons l'emploi d'une estimation � base de r�gression flexible du mod�le h�donique � d�calage spatial, tenant compte de diff�rents effets des caract�ristiques, et surtout de diff�rents degr�s de corr�lations s�rielles spatiales. En appliquant ce principe � un �chantillon de ventes de terrains agricoles en Irlande du Nord, nous d�couvrons que le march� se compose de deux segments relativement distincts. Le plus important de ces deux segments est conforme au mod�le h�donique traditionnel, sans d�pendance du d�calage spatial, tandis que le deuxi�me segment du march�, plus petit et beaucoup plus �troit, pr�sente une d�pendance consid�rable du d�calage spatial. Un modelo hed�nico de regresi�n cuantil espacial de los precios del terreno agr�cola Resumen T�picamente, los estudios del precio de la tierra emplean un an�lisis hed�nico para identificar el impacto de las caracter�sticas de la tierra sobre el precio. No obstante, debido a la fijeza espacial de la tierra, surge la cuesti�n de una posible dependencia espacial en los precios del terreno agr�cola. La presencia de dependencia espacial en los precios del terreno agr�cola puede tener consecuencias graves para el modelo de an�lisis hed�nico. Ignorar la autocorrelaci�n espacial puede conducir a estimados parciales en los modelos hed�nicos del precio de la tierra. Proponemos el uso de una valoraci�n basada en una regresi�n cuantil flexible del modelo hed�nico del lapso espacial que tenga en cuenta los diversos efectos de las caracter�sticas y, particularmente, los diversos grados de autocorrelaci�n espacial. Al aplicar este planteamiento a una muestra de ventas de terreno agr�cola en Irlanda del Norte, descubrimos que el mercado consiste efectivamente de dos segmento relativamente separados. El m�s grande de estos dos segmentos se ajusta al modelo hed�nico convencional sin dependencia del lapso espacial, mientras que el segmento m�s peque�o, y mucho m�s fino, muestra una dependencia considerable del lapso espacial.Spatial lag, quantile regression, hedonic model, C13, C14, C21, Q24,

Semiparametric Construction of Spatial Generalized Hedonic Models for Private Properties

This paper analyzes the empirical hedonic prices for non-standard condominiums and single family houses using nonparametric estimates as well as a generalized additive model for the spatial generalization of the attractiveness of all Swiss communities. We find that the assumption of log-linearity for continuous variables does not hold but can be replaced by partwise log-linear or quadratic terms. Due to the topographical segmentation of Switzerland, driving times seem to be more adequate than geographical distances to explain the price level of a village with the price level of its neighbours. We show, that using metric multidimensional scaling, the driving times between the villages can be converted into artificial coordinates with three principal axis to serve as a basis for the prediction of the macro-locations of the Swiss villagesHedonic prices; private property; Switzerland; robust regression; splines; multidimensional scaling

Alternative methods for quantifying commuting-related benefits of new transport infrastructure

A variety of methods have been developed which allow the estimation of benefits likely to arise from new transport infrastructure. In this paper, we concentrate on measuring commuting-related benefits. We compare and contrast two different approaches. The first relies on using data on commuting flows and the gravity model. The second approach uses the relationship between labour market accessibility and house prices. We use both methods to quantify these benefits, and discuss some of the potential reasons why they may give different estimates. We take as our case study a large infrastructure project in south-west Norway.

Landscape value in the spanish Costa del Sol’s real estate market: the case of Marbella

Housing prices are influenced by extrinsic and intrinsic factors. This study aims to highlight the economic impact of the perceived landscape on single-family houses prices in a Spanish Mediterranean urban area (Marbella). Considering the landscape an important added value in real estate markets, this study also explores the landscape elements that contribute the most to the value of housing. A particularly positive influence of mixed views (urban elements and Mediterranean scrub) and sea views is detected in the analysis. Sea views are highly requested in the local housing market, but due to the graded topographical layout of Marbella, it is not very difficult to have sea views for houses. The low importance of views on natural land areas is worth noting when one of the attractions of this municipality is that of a highly valued Mediterranean natural environment. Views on the old town centre are somewhere in between: although the old town centre is highly regarded, with a generally good state of preservation, the sampled properties have poorer quality perspectives, with reduced visual basins and views centred on the foreground, usually the houses opposite.Partial funding for open access charge: Universidad de Málag

The Impact of Wetlands Rules on the Prices of Regulated and Proximate Houses: A Case Study

Federal, state and local wetlands protection laws that restrict landowners’ ability to develop their properties in certain ways could decrease the value of the affected properties. However, the regulations could also give benefits to nearby neighbors who no longer need worry about increased development in their area. Given that some properties may decline in value, while others increase, the impact on individual properties must be determined empirically. This study uses a data set from Newton, Massachusetts to examine the impact of wetlands laws on the regulated properties, as well as on proximate properties. Looking at house sales data from 1988 through 2005, the hedonic technique is used to estimate the effect of wetlands regulations on single family home prices and finds that having wetlands on a property decreases its value by 4% relative to non-regulated properties. Homes that are contiguous to regulated houses do not experience any change in price. Thus it seems unlikely that neighbors are receiving any benefit from knowing that further development is restricted in their immediate vicinity.Environment, housing, amenities, hedonic pricing, wetlands

Applying a CART-based approach for the diagnostics of mass appraisal models

In this paper an approach for automatic detection of segments where a regression model significantly underperforms and for detecting segments with systematically under- or overestimated prediction is introduced. This segmentational approach is applicable to various expert systems including, but not limited to, those used for the mass appraisal. The proposed approach may be useful for various regression analysis applications, especially those with strong heteroscedasticity. It helps to reveal segments for which separate models or appraiser assistance are desirable. The segmentational approach has been applied to a mass appraisal model based on the Random Forest algorithm.CART, model diagnostics, mass appraisal, real estate, Random forest, heteroscedasticity

On the hedonic modelling of land prices

In this study hedonic modelling methods beyond the ordinary least squares estimator are investigated in explaining and predicting the land prices in the two submarkets (Espoo and Nurmijärvi) of the Finnish land markets. The first paper deals with the estimation of several parametric hedonic models, including dynamic responses, using recursive estimation technique. The second paper examines the applicability of semiparametric structural time series methods to the optimal estimation of spatio-temporal movements of land prices. The third paper focuses on the robust nonparametric estimation using local polynomial modelling approach in explaining and predicting the land prices. The fourth paper investigates flexible wavelet transforms in the estimation of long-run temporal land price movements (cycles and trends). The final fifth paper uses robust parametric estimator, the three-stage MM-estimator, to explicitly address the problem of outlying and influential data points. The key observation of this study is that there is much scope for methods beyond the ordinary least squares estimator in explaining and predicting the land prices in local markets. This is especially true in the submarket of Espoo, where the use of unconventional methods of the study showed that significant improvements could be achieved in hedonic models' explanatory power and/or predictive validity when the methods of this research are used instead of the orthodox least squares estimator. In the Espoo case structural time series models, local polynomial regression and robust MM-estimation all generated more precise results in terms of post-sample prediction power than the conventional least squares estimator. The empirical experimentation quite strongly indicated that the determination of land prices in the municipality of Nurmijärvi could be best explained by the use of unobserved component models. The flexible local polynomial modelling and three-stage MM-estimation surprisingly added no value in terms of greater post-sample precision in the Nurmijärvi case.Tässä tutkimuksessa tarkastellaan sellaisia hedonisia mallintamismenetelmiä, jotka yleistävät tavallisen pienimmän neliösumman mukaista ratkaisua, kun selitetään ja ennustetaan maanhintoja kahdella osamarkkina-alueella (Espoo ja Nurmijärvi) Suomen maamarkkinoilla. Ensimmäinen artikkeli tarkastelee erilaisten parametristen mallien estimointia käyttämällä rekursiivista estimointitekniikkaa. Toinen artikkeli tutkii semiparametristen rakenteellisten aikasarjamallien soveltuvuutta ajallis-paikallisten maanhintavaihteluiden optimaalisessa estimoinnissa. Kolmas artikkeli keskittyy vikasietoiseen ja ei-parametriseen estimointiin käyttämällä paikallisia polynomimalleja, kun selitetään ja ennustetaan maanhintoja. Neljäs artikkeli tutkii joustavia aalloke-muunnoksia pitkän ajanjakson maanhintojen vaihteluiden (syklien ja trendien) estimoinnissa. Viimeinen viides artikkeli käyttää vikasietoista parametrista estimaattoria, kolmivaiheista MM-estimaattoria, vähentämään mallintamisessa ilmenevien poikkeavien ja vaikutusvaltaisten havaintopisteiden negatiivinen vaikutus. Tutkimuksen avainhavainto on, että tutkimuksessa tarkasteltuja epästandardeja menetelmiä voidaan soveltaa hyvin käytännön ongelmaratkaisutilanteissa, kun selitetään ja ennustetaan maanhintoja paikallisilla markkinoilla. Tämä pätee erityisesti Espoon hinta-aineistolla, jossa epästandardien menetelmien käyttö johti hedonisiin hintamalleihin, jotka omasivat huomattavasti korkeamman selitysvoimakkuuden ja/tai ennustustarkkuuden kuin tavallisen pienimmän neliösumman mukainen ratkaisu. Espoon osamarkkinoiden tapauksessa rakenteelliset aikasarjamallit, vikasietoinen paikallinen regressioanalyysi ja vikasietoinen MM-estimointi tuottivat tarkempia tuloksia kuin perinteinen pienimmän neliösumman mukainen keino, kun estimoitujen mallien hyvyyttä arviointiin ennustustarkkuuden mielessä eri kriteereillä. Empiirinen tutkimus indikoi varsin voimakkaasti, että Nurmijärven osamarkkinoiden tapauksessa maanhinnan muodostus voitiin parhaiten selittää käyttämällä rakenteellisia aikasarjamalleja. Sen sijaan joustavat polynomimallit ja MM-estimointi eivät tuoneet lisäarvoa mallien paremman ennustustarkkuuden valossa Nurmijärven hinta-aineistolla.reviewe

Real estate appraisals with Bayesian approach and Markov Chain Hybrid Monte Carlo Method: An application to a central urban area of Naples

This paper experiments an artificial neural networks model with Bayesian approach on a small real estate sample. The output distribution has been calculated operating a numerical integration on the weights space with the Markov Chain Hybrid Monte Carlo Method (MCHMCM). On the same real estate sample, MCHMCM has been compared with a neural networks model (NNs), traditional multiple regression analysis (MRA) and the Penalized Spline Semiparametric Method (PSSM). All four methods have been developed for testing the forecasting capacity and reliability of MCHMCM in the real estate field. The Markov Chain Hybrid Monte Carlo Method has proved to be the best model with an absolute average percentage error of 6.61%