Wolfgang Brunauer

Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria

Modelling spatial heterogeneity (SH) is a controversial subject in real estate economics. Single-family-home prices in Austria are explored to investigate the capability of global and locally weighted hedonic models. Even if regional indicators are not fully capable to model SH and technical amendments are required to account for unmodelled SH, the results emphasise their importance to achieve a well-specified model. Due to SH beyond the level of regional indicators, locally weighted regressions are proposed. Mixed geographically weighted regression (MGWR) prevents the limitations of fixed effects by exploring spatially stationary and non-stationary price effects. Besides reducing prediction errors, it is concluded that global model misspecifications arise from improper selected fixed effects. Reported findings provide evidence that the SH of implicit prices is more complex than can be modelled by regional indicators or purely local models. The existence of both stationary and non-stationary effects implies that the Austrian housing market is economically connected.

Modeling House Prices using Multilevel Structured Additive Regression

This paper analyzes house price data belonging to three hierarchical levels of spatial units. House selling prices with associated individual attributes (the elementary level-1) are grouped within municipalities (level-2), which form districts (level-3), which are themselves nested in counties (level-4). Additionally to individual attributes, explanatory covariates with possibly nonlinear effects are available on two of these spatial resolutions. We apply a multilevel version of structured additive regression (STAR) models to regress house prices on individual attributes and locational neighbourhood characteristics in a four-level hierarchical model. In multilevel STAR models the regression coefficients of a particular nonlinear term may themselves obey a regression model with structured additive predictor. The framework thus allows to incorporate nonlinear covariate effects and time trends, smooth spatial effects and complex interactions at every level of the hierarchy of the multilevel model. Moreover, we are able to decompose the spatial heterogeneity effect and investigate its magnitude at different spatial resolutions allowing for improved predictive quality even in the case of unobserved spatial units. Statistical inference is fully Bayesian and based on highly efficient Markov chain Monte Carlo simulation techniques that take advantage of the hierarchical structure in the data.

Data-Driven Regionalization of Housing Markets

This article presents a data-driven framework for housing market segmentation. Local marginal house price surfaces are investigated by means of mixed geographically weighted regression and are reduced to a set of principal component maps, which in turn serve as input for spatial regionalization. The out-of-sample prediction error of a hedonic pricing model is applied to determine a “near-optimal” number of spatially coherent and homogeneous submarkets. The usefulness of this method is demonstrated with a detailed data set for the Austrian housing market. The results provide evidence that submarkets must always be considered, however they are defined, and that the proposed submarket taxonomy on a regional level significantly improves predictive quality compared to (1) a traditional pooled model, (2) a model that uses an ad hoc submarket definition based on administrative units, and (3) a model incorporating an alternative submarket definition on the basis of aspatial k-means clustering. Moreover, it is concluded that the Austrian housing market is characterized by regional determinants and that geography is the most important component determining the house prices.

THE MORTGAGE LENDING VALUE: PROSPECTS FOR DEVELOPMENT WITHIN EUROPE

Purpose – The purpose of this paper is to critically review the German mortgage lending value (MLV) and to adapt it in order to find a new concept that could serve as the basis for an internationally accepted standard for valuations for lending purposes.Design/methodology/approach – The research is based on a critical review of existing practices and literature and applies developments in the area of risk management tools, modern valuation techniques as well as the results of the consultation for Basel II in order to find an improved method.Findings – It was found that a value‐at‐risk approach and the implementation of simulation helps to understand the concept of MLV. The results also indicate that the German system of calculating the MLV has to be improved.Practical implications – Banks are in need of tools, reliable instruments and a strong theoretical basis when evaluating their collateral. The valuation of real estate for long‐term loans has always been a problem. This paper indicates a strong basis ...

How does energy efficiency influence the Market Value of office buildings in Germany and does this effect increase over time

Purpose – The paper aims to estimate the effect of energy efficiency on the Market Value of office buildings and consider whether this effect increases over time. Design/methodology/approach – The authors analyze a dataset of office building valuations from 2009 to 2011, provided by the German Investment Property Database. The authors use hedonic regression models to determine the effect of energy efficiency and energy consumption on Market Values. Using generalized additive models (GAM) for modeling nonlinear covariate effects, the authors control for further building characteristics and location. Due to the small sample size, the authors introduce an innovative econometric approach that mitigates this problem. Findings – Mainly due to the small sample size, and in spite of the newly developed econometric methodology, the authors do not find clear evidence of the relationship between energy efficiency and the Market Value. However, the study nonetheless provides interesting insights into the composition ...

The energy efficiency of corporate real estate assets: The role of energy management for corporate environmental performance

Purpose On the basis of corporate wholesale and hypermarket stores, this study aims to investigate the relationship between energy consumption, physical building characteristics and operational sales performance and the impact of energy management on the corporate environmental performance. Design/methodology/approach A very unique dataset of METRO GROUP over 19 European countries is analyzed in a sophisticated econometric approach for the timeframe from January 2011 until December 2014. Multiple regression models are applied for the panel, to explain the electricity consumption of the corporate assets on a monthly basis and the total energy consumption on an annual basis. Using Generalized Additive Models, to model nonlinear covariate effects, the authors decompose the response variables into the implicit contribution of building characteristics, operational sales performance and energy management attributes, under control of the outdoor weather conditions and spatial–temporal effects. Findings METRO GROUP’s wholesale and hypermarket stores prove significant reductions in electricity and total energy consumption over the analyzed timeframe. Due to the implemented energy consumption and carbon emission reduction targets, the influence of the energy management measures, such as the identification of stores associated with the lowest energy performance, was found to contribute toward a more efficient corporate environmental performance. Originality/value In the context of corporate responsibility/sustainability of wholesale, hypermarket and retail corporations, the energy efficiency and reduction of carbon emissions from corporates’ real estate assets is of emerging interest. Besides the insights about the energy efficiency of corporate real estate assets, the role of the energy management, contributing to a more efficient corporate environmental performance, is not yet investigated for a large European wholesale and hypermarket portfolio.

Methodical Revision of the Austrian Residential Property Price Index

Price movements in the residential property market are often small-scale and can lead to regional over- / undervaluations that cannot be reported on an aggregated level. However, the derivation of regionalized price developments demands for appropriate methods and sufficient big data sample sizes. Therefore, the Austrian residential property price Index, which is published quarterly by Oesterreichische Nationalbank (OeNB), has been methodically revised: Instead of the previously used time dummy approach, which only allows for an evaluation at the model region level, the new index is based on double imputation methods, which makes possible an almost arbitrary regionalization. In this study, we describe the impact of the methodical changes on the index developments on an aggregated level. Furthermore, the new models allow a more granular regional breakdown of the index series. The resulting data series can be used for a more detailed monitoring of developments in the residential property market and can serve even as an input for a more granular regionalized fundamental indicator for residential property prices.

Hedonic House Price Modeling Based on Multilevel Structured Additive Regression

This chapter reviews recent developments in hedonic modeling of house prices based on structured additive regression (STAR) models. In STAR models, continuous covariates are modeled as P(enalized)-splines. Furthermore, random effects for spatial indexes, smooth functions of two-dimensional surfaces, and (spatially) varying coefficient terms may also be estimated using this methodology. Based on hierarchical STAR models, we discuss a number of useful extensions. With respect to value-at-risk concepts, financial institutions are often not only interested in the expected value but also in different quantiles of the distribution of real estate prices. To meet these requirements, we apply multilevel STAR models for location scale and shape (GAMLSS type regression) and a Bayesian version of quantile regression. As another extension, we sketch multiplicative region-specific scaling factors for nonlinear covariates in order to permit spatial variation in the nonlinear price gradients.

The Energy Efficiency of Corporate Real Estate Assets: The Role of Professional Management for Corporate Environmental Performance

Purpose – On the basis of corporate wholesale and hypermarket stores, this study investigates the relationship between energy consumption, physical building characteristics, operational sales performance and the impact of energy management on the corporate environmental performance. Design/methodology/approach – A very unique dataset of METRO GROUP over 19 European countries is analyzed in a sophisticated econometric approach for the timeframe from January 2011 until December 2014. Multiple regression models are applied for the panel, to explain the electricity consumption of the corporate assets on a monthly basis and the total energy consumption on an annual basis. Using Generalized Additive Models (GAM), to model nonlinear covariate effects, the authors decompose the response variables into the implicit contribution of building characteristics, operational sales performance and energy management attributes, under control of the outdoor weather conditions and spatial-temporal effects. Findings – METRO GROUP’s wholesale and hypermarket stores prove significant reductions in electricity and total energy consumption over the analyzed timeframe. Due to the implemented energy consumption and carbon emission reduction targets, the influence of the energy management measures, such as the identification of stores associated with lowest energy performance, was found to contribute towards a more efficient corporate environmental performance. Originality/value – In the context of corporate responsibility/sustainability of wholesale, hypermarket and retail corporations, the energy efficiency and reduction of carbon emissions from corporates real estate assets is of emerging interest. Beside insights about the energy efficiency of corporate real estate assets, the role of the energy management, contributing to a more efficient corporate environmental performance, is not yet investigated for a large European wholesale and hypermarket portfolio.