Road distance and travel time for an improved house price Kriging predictor

The paper designs an automated valuation model to predict the price of residential property in Coventry, United Kingdom, and achieves this by means of geostatistical Kriging, a popularly employed distance-based learning method. Unlike traditional applications of distance-based learning, this papers implements non-Euclidean distance metrics by approximating road distance, travel time and a linear combination of both, which this paper hypothesizes to be more related to house prices than straight-line (Euclidean) distance. Given that – to undertake Kriging – a valid variogram must be produced, this paper exploits the conforming properties of the Minkowski distance function to approximate a road distance and travel time metric. A least squares approach is put forth for variogram parameter selection and an ordinary Kriging predictor is implemented for interpolation. The predictor is then validated with 10-fold cross-validation and a spatially aware checkerboard hold out method against the almost exclusively employed, Euclidean metric. Given a comparison of results for each distance metric, this paper witnesses a goodness of fit (r²) result of 0.6901 ± 0.18 SD for real estate price prediction compared to the traditional (Euclidean) approach obtaining a suboptimal r² value of 0.66 ± 0.21 SD

Nonparametric correlogram to identify the geographic distance of spatial dependence on land prices

The spatial autocorrelation measurement of land prices uses a covariance function to describe the spatial dependence and it can be identified as a geographic distance on the correlogram. The geographic distance of spatial dependence can state that land prices are interdependent to each other and scattered in the research area. Therefore, the purpose of this research is to define the geographic distance of spatial dependence on land prices using a nonparametric correlogram. A nonparametric approach to covariance functions using the composition of Bessel and Gaussian-type functions are adopted because they correspond to the positive definite characteristics. The cubic spline interpolation is used to refine the curve fitting, while the intersection between the nonparametric correlogram value C(h) against the horizontal axis is determined using the Jenkins Traub algorithm. The results showed that the nonparametric correlogram identified a geographic distance of land prices smaller than the correlogram used so far. A small distance means that the land price in a location is greatly affected by the neighbors compared to a larger distance

Embedding road networks and travel time into distance metrics for urban modelling

Urban environments are restricted by various physical, regulatory and customary barriers such as buildings, one-way systems and pedestrian crossings. These features create challenges for predic- tive modelling in urban space, as most proximity-based models rely on Euclidean (straight line) distance metrics which, given restrictions within the urban landscape, do not fully capture spa- tial urban processes. Here, we argue that road distance and travel time provide effective alternatives, and we develop a new low- dimensional Euclidean distance metric based on these distances using an isomap approach. The purpose of this is to produce a valid covariance matrix for Kriging. Our primary methodological contribution is the derivation of two symmetric dissimilarity matrices (Bþ and B2þ), with which it is possible to compute low- dimensional Euclidean metrics for the production of a positive definite covariance matrix with commonly utilised kernels. This new method is implemented into a Kriging predictor to estimate house prices on 3,669 properties in Coventry, UK. We find that a metric estimating a combination of road distance and travel time, in both R 2 and R 3 , produces a superior house price predictor compared with alternative state-of-the-art methods, that is, a standard Euclidean metric in RN and a non-restricted road dis- tance metric in R2 and R3

Modelos automatizados de avaliação de imóveis: Aplicação à Área Metropolitana de Lisboa

A avaliação fornece uma medida dos benefícios e responsabilidades de um imóvel e, desde sempre, depende muito dos métodos tradicionais, aplicados por peritos avaliadores. Apesar destes resistirem à evolução dos métodos de previsão do valor, têm surgido sistemas especializados na determinação do mesmo, os modelos automatizados de avaliação (AVM), estimando-se que já serviram mais de 100 milhões de entidades. Os AVM são programas informáticos que estimam o valor dos imóveis através da análise de atributos relacionados com os imóveis. Além disso, os peritos são acusados de subjetividade nas avaliações, surgindo a necessidade de melhorar a qualidade da previsão através de sistemas independentes. Assim, esta dissertação foca-se na construção de modelos preditivos com dados de uma plataforma imobiliária, da Área Metropolitana de Lisboa, recorrendo a técnicas de data mining. Para se construir modelo, procede-se a uma análise estatística, como forma de ajustamento da base de dados, e aplicam-se técnicas preditivas. A utilização de diversos algoritmos visa a obtenção do melhor modelo possível, com a melhor capacidade preditiva do valor do imóvel, e tendo como variáveis preditoras as características do imóvel e da sua localização. O melhor modelo obtido, é uma rede neuronal e apresenta um erro médio de previsão de 0,06 e um coeficiente de determinação de 0,90. Um modelo eficaz é uma ferramenta muito útil de apoio aos stakeholders, sendo eles peritos, compradores, vendedores, bancos, Estado, entre outros. Em suma, esta investigação é muito importante para o setor imobiliário, que tem muita importância em qualquer economia.Valuation provides a measure of the benefits and responsibilities of a property and has always depended heavily on traditional methods applied by expert appraisers. Despite they resist the evolution of value forecasting methods, specialized systems have emerged to determine the value, the automated valuation models (AVM), estimated to have served more than 100 million entities. AVMs are computer programs that estimate the value of properties through the analysis of attributes related to the properties. In addition, experts are accused of subjectivity in valuations, which raises the need to improve the quality of forecasting through independent systems. Thus, this dissertation focuses on the construction of predictive models with data from a real estate platform, in the Lisbon Metropolitan Area, using data mining techniques. In order to build a model, statistical analyzes is carried out, as a way of adjusting the database, and predictive techniques are be applied. The use of these techniques aims to obtain the best possible model, with the best predictive capacity of the value of the property and having as predictors the characteristics of the property and its location. The best model obtained is a neural network and presents an average prediction error of 0.06 and a coefficient of determination of 0.90. An effective model is a very useful tool to support stakeholders, including experts, buyers, sellers, banks, the State, among others. In short, this investigation is very important for the real estate sector, which is very important in any economy