Contextualized property market models vs. Generalized mass appraisals: An innovative approach

The present research takes into account the current and widespread need for rational valuation methodologies, able to correctly interpret the available market data. An innovative automated valuation model has been simultaneously implemented to three Italian study samples, each one constituted by two-hundred residential units sold in the years 2016-2017. The ability to generate a "unique" functional form for the three different territorial contexts considered, in which the relationships between the influencing factors and the selling prices are specified by different multiplicative coefficients that appropriately represent the market phenomena of each case study analyzed, is the main contribution of the proposed methodology. The method can provide support for private operators in the assessment of the territorial investment conveniences and for the public entities in the decisional phases regarding future tax and urban planning policies

Road pricing and (re)location decisions households

Road pricing policies are, after a cooling down period of a couple of years, again prominently back on the political agenda in the Netherlands. But also in the period of less political interest, research in the field of (road) pricing policies continued in other countries as well as in the Netherlands. Most research literature focuses on the economic and acceptability aspects of pricing policies. The geographical aspects of transport pricing however have received much less attention so far. This paper focuses on possible influences of road pricing policies on residential and work location choice of households. The paper starts with analyzing the importance of transport and location related variables in residential location decisions, when the choice to relocate itself has been made. For this analysis data from a stated choice experiment is used. Choice screens within the experiment consisted of two alternatives. In total, per respondent nine choice screens were shown. Transport related variables within the experiment were commuting travel time, fuel cost and toll cost. The location related variables consisted of the residential environment, the number of bedrooms and the monthly housing costs. Analysis of the results gives insight into the importance of for example toll costs on the final location choice when a decision to relocate itself has already been made. However this type of analysis does not give insight into the number of people who are actually considering changing location when a form of road pricing is introduced. Therefore the second part of the paper continues with analyzing the extent to which people are likely to relocate due to road pricing. The effect of different types of pricing measures and pricing levels on this inclination to relocate are examined. For the analysis, data from a stated preference questionnaire is used. The paper finally concludes with an examination of important explaining variables for moving house or changing job due to road pricing. Some important findings are for example that older people (above 40 years of age), people with a higher income and persons getting a travel cost compensation from their employer are less willing to move due to a pricing measure. People with a higher education level however are more willing to relocate.

Housing Market in Malaga: An Application of the Hedonic Methodology

The analysis of the factors that determine the price of the second-hand house, by means of the use of the hedonic methodology, constitutes the central objective of this work. This study has been applied to the market of the house corresponding to the municipality of Malaga (Spain), of where a sample of 1996 transactions, made during 2003, has been selected.This information has been facilitated by a real estate agency. The obtained results have allowed to identify those characteristics of the houses that more affect their price and quantify this influence, valuing it in monetary terms. It has been stated that the contribution of some structural attributes (the floor area, the number of toilets, the presence of private garage or the luminosity of the house) and others of location (proximity to the sea or downtown, and location in a certain zone) affects the price of the house decisively.

Prices and Constant Quality Price Indexes for Multi-Dwelling and Commercial Buildings in Sweden

The purpose of this paper is to estimate constant quality price trends and analysing factors determining market prices for MDCBs (multi-dwelling and commercial buildings) in Sweden. We use high quality data for housing and municipality attributes and our database consists of almost 8500 observations from the second half of 1995 to the end of 1998. Our econometric test indicates that standard housing and municipality attributes are important determinants to sales prices. We have also employed spatial econometric techniqes and have found that spatial specified regressions improved the explanatory power for the models. The estimated constant quality appreciation rates for all MDCBs differ significantly from those reported by Statistics Sweden. When the constant quality price trend is estimated on a yearly basis there are hardly any differences among the estimated parameters whether all MDCBs are in the sample or if the sample is split up into submarkets. However, estimating quarterly constant quality price trends gives another picture.House price; hedonic modelling; constant quality price index; spatial econometrics

SpICE: An interpretable method for spatial data

Statistical learning methods are widely utilized in tackling complex problems due to their flexibility, good predictive performance and its ability to capture complex relationships among variables. Additionally, recently developed automatic workflows have provided a standardized approach to implementing statistical learning methods across various applications. However these tools highlight a main drawbacks of statistical learning: its lack of interpretation in their results. In the past few years an important amount of research has been focused on methods for interpreting black box models. Having interpretable statistical learning methods is relevant to have a deeper understanding of the model. In problems were spatial information is relevant, combined interpretable methods with spatial data can help to get better understanding of the problem and interpretation of the results. This paper is focused in the individual conditional expectation (ICE-plot), a model agnostic methods for interpreting statistical learning models and combined them with spatial information. ICE-plot extension is proposed where spatial information is used as restriction to define Spatial ICE curves (SpICE). Spatial ICE curves are estimated using real data in the context of an economic problem concerning property valuation in Montevideo, Uruguay. Understanding the key factors that influence property valuation is essential for decision-making, and spatial data plays a relevant role in this regard

Land valuation using an innovative model combining machine learning and spatial context

Valuation predictions are used by buyers, sellers, regulators, and authorities to assess the fairness of the value being asked. Urbanization demands a modern and efficient land valuation system since the conventional approach is costly, slow, and relatively subjective towards locational factors. This necessitates the development of alternative methods that are faster, user-friendly, and digitally based. These approaches should use geographic information systems and strong analytical tools to produce reliable and accurate valuations. Location information in the form of spatial data is crucial because the price can vary significantly based on the neighborhood and context of where the parcel is located. In this thesis, a model has been proposed that combines machine learning and spatial context. It integrates raster information derived from remote sensing as well as vector information from geospatial analytics to predict land values, in the City of Springfield. These are used to investigate whether a joint model can improve the value estimation. The study also identifies the factors that are most influential in driving these models. A geodatabase was created by calculating proximity and accessibility to key locations as well as integrating socio-economic variables, and by adding statistics related to green space density and vegetation index utilizing Sentinel-2 -satellite data. The model has been trained using Greene County government data as truth appraisal land values through supervised machine learning models and the impact of each data type on price prediction was explored. Two types of modeling were conducted. Initially, only spatial context data were used to assess their predictive capability. Subsequently, socio-economic variables were added to the dataset to compare the performance of the models. The results showed that there was a slight difference in performance between the random forest and gradient boosting algorithm as well as using distance measures data derived from GIS and adding socioeconomic variables to them. Furthermore, spatial autocorrelation analysis was conducted to investigate how the distribution of similar attributes related to the location of the land affects its value. This analysis also aimed to identify the disparities that exist in terms of socio-economic structure and to measure their magnitude.Includes bibliographical references

Factors Affecting Spatial Autocorrelation in Residential Property Prices

Within housing literature, the presence of spatial autocorrelation (S.A.) in housing prices is typically examined horizontally in a two-dimensional setting. However, in the context of apartment buildings, there is also a vertical component of S.A. for housing units located on different floor levels. This paper therefore explores the determinants of both horizontal and vertical S.A. within residential property prices. First, we posit that S.A. in housing prices is a consequence of the price discovery process of real estate, in which property traders acquire price information from recent market transactions (i.e., comparables) to value a subject property. Furthermore, we contend that the extent to which property traders rely on comparables to determine housing prices is governed by the liquidity and volatility conditions of the market, which in turn affects the magnitude of the S.A. By developing and testing several spatial autoregressive hedonic models using open market transaction data for the Hong Kong residential property market, we find that market liquidity tends to increase both vertical and horizontal S.A., whilst market volatility is more prone to increase vertical S.A. but depress horizontal S.A