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

Non-linear and weakly monotonic relationship between school quality and house prices

This study provides evidence for a non-linear and weakly monotonic relationship between school quality and house prices. Using Fremont, California, as the study area, the regression analysis shows that homeowners are unwilling to pay a premium for an increase in school quality from low to medium quality. However, they are willing to pay a) a large premium when all schools are top-quality schools and b) a premium for access to nationally-renowned schools, which is in addition to the premium for top-quality schools. These findings have important land use policy significance because they provide new insights into the homeowner’s residential location choice and highlight the need to consider school quality in a jurisdiction’s land use and zoning decisions

Three Essays on Estimating and Forecasting Residential Markets

This dissertation covers three different aspects of estimating and forecasting residential real estate markets. Chapter 1: The first chapter aims to examine whether there are differences between the long and short-term relationship of house prices and interest rates. The elasticity of house prices to monetary policy changes, e.g. via interest rates, is from a theoretical perspective and in the long-run negative. However, house prices adapt in the short-run dynamically to economic, financial, institutional and demographic factors. In this chapter, we confirm the aforementioned negative relationship for the Nordic housing markets but provide evidence of drastic deviations in the short run, especially in the context of the financial crisis. Chapter 2: The second chapter tests the prediction accuracy of two innovative methods proposed along the hedonic debate: The Geographically Weighted Regression (GWR) and the Generalized Additive Model (GAM). We compare the predictions of linear, spatial and non-linear hedonic models based on a very large dataset in Germany. The results provide evidence for a clear disadvantage of the GWR model in out-of-sample forecasts. However, the simplicity of the OLS approach is not substantially outperformed by the semi-parametric approach. Since sample size is essential when estimating and forecasting hedonic models this study covers more than 570,000 observations which is – to the authors' knowledge – one of the largest datasets used for spatial real estate analysis. Chapter 3: Google Trends offers virtually unlimited, instantaneously available, spatially and textually adjustable and, in addition, free data. Real estate markets appear to be particularly well-suited for search volume related studies, as the “products” of this market involve a large financial commitment, which demands an extensive information gathering process. Although Google Trends data can be accessed already since 2008, many interpretation and usage misunderstandings can be found amongst the literature. Therefore, the third chapter will focus on two main objectives: Firstly, I will give an overview of what Google data is in the first place and what the potential pitfalls are. Secondly, I will conduct an empirical analysis to find out, whether the results are still in line with the literature after accounting for those difficulties

Spatial processes in environmental economics: empirics and theory

Economic activities are fundamentally influenced by their location in space, which determines the physical and natural environment in which they take place. Likewise, location defines the social context of economic activity prescribing the particular laws, regulations and social norms to which it should conform. Moreover, spatial location defines proximity, which shapes the costs of accessing factor inputs, product markets and other economic and social institutions. In fact, spatial location mediates most forms of interaction, intended and unintended, that may arise from communication and connections between economic agents. These spatial processes have important implications for estimation, policy evaluation and prediction in models of economic activity. This thesis is comprised of two parts. Part I presents a broad range of issues that arise in estimation due to space and frames these as general spatial omitted variables. I explore the use of semi-parametric estimators to identify the parameters of interest in this general model and derive identification conditions for fixed and local adaptive spatial smoothing estimators. The properties of these estimators are contrasted to OLS and spatial econometric estimators. Part II addresses issues in policy evaluation and prediction. I derive an equilibrium sorting model with endogenous tenure choice that can be used to evaluate the general equilibrium welfare effects of policies that affect local environmental quality. Using a series of simulations, motivated by a real world policy application, I contrast the welfare changes derived under this model to a conventional static approach. By allowing for rental and purchase markets the model I develop provides a far richer characterisation of the complex adjustments that propagate through the property market following policy changes and the contrary impact such policies can have upon renters and owners. The usefulness of the model for applied policy analysis is demonstrated through two applications: The Polegate Bypass and Mortgage Interest Deduction reform

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Hedonic analysis of property markets: theory and applications to UK cities

This thesis concerns the hedonic analysis of property markets. In particular, it investigates the extent to which such analysis can reveal household preferences for the avoidance of exposure to transport-related noise pollution. The thesis is divided into three Parts. Part 1 provides a thorough exposition of the economic theory of property markets. It contains two chapters. The first details the establishment of a market-clearing hedonic price equilibrium. The second outlines how the property market model can be used to identify measures of welfare change resulting from exogenous changes in environmental quality. As well as providing possibly the most complete and coherent exposition of this expansive and occasionally confused literature, Part 1 also contributes new insights into welfare measurement when landlords are constrained in their responses to environmental change. The following two Parts of the thesis concern empirical applications of hedonic analysis to property markets. Part 2 is concerned with the estimation of hedonic price functions for the City of Birmingham property market. The unique innovations presented here include the application of techniques for partitioning data in order to improve specification of the hedonic price function and the application of semiparametric estimators in order to redress spatial autocorrelation amongst regression residuals. Finally Part 3 of the thesis concerns itself with welfare analysis. Specifically, it provides a thorough discussion of the implications of the theory in Part 1 for empirical estimation of preference parameters. Following these empirical guidelines and drawing on results from Part 2, welfare estimates for changes in exposure to traffic-related noise pollution are provided. As far as the author is aware, these are the first welfare estimates for noise pollution to be derived from a hedonic analysis in a theoretically consistent manner

Mekansal ve mekansal olmayan tekniklere dayalı konut değerleme modellerinin incelenmesi.

The aim of this thesis is to develop hedonic housing valuation models based on spatial (SAR-simultaneous spatial autoregression and GWR - geographically weighted regression) and non-spatial (OLS - ordinary least squares) techniques, to compare the performances of these models and to investigate significant factors affecting housing value. The developed housing valuation models were tested at the Çankaya and Keçiören districts of Ankara province, Turkey. The results of the analyses revealed that significant spatial non-stationarity exists between the dependent and independent variables. A semi-logarithmic hedonic model was used in order to interpret the coefficients easily and minimize the problem of heteroscedasticity. The results show that Area, Security and Distance to Shopping Center are common significant factors for both Çankaya and Keçiören districts in Ankara. Other important factors are the Type of Property and Distance to Subway for Çankaya and the Floor and Household variables for Keçiören. The SAR and the GWR spatial models gave a better approximation to the observed house values than the traditional non-spatial regression model. The SAR model showed the best performance in Çankaya and the GWR model indicated high performance in Keçiören. The GWR maps displayed the variation of the coefficients of each variable clearly.Ph.D. - Doctoral Progra