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The development and conditions of the housing market are an important part of economic research, receiving growing attention also in Germany. The RWI-GEO-RED dataset covers data for the German residential housing market from 2007 onwards on 1 km² level and closes the gap for small-scale, nationwide and up-to-date housing data for Germany. The data has a large potential in analyzing the real estate market itself or to evaluate policy interventions through treatments on small regional levels. Prominent examples are the closing of nuclear power plants and the German rent control. The dataset can be used on its own but also has a large potential for combination with different data. The dataset is provided by the FDZ Ruhr at RWI that hosts small-scale regional data for Germany. The data can be obtained for scientific research as scientific use file. [ABSTRACT FROM AUTHOR]

In this paper, we present HomeSeeker, an interactive visual analytics system to serve users with different backgrounds of the local real estate market and meet different degrees of user requirements. As a result, HomeSeeker augments existing commercial systems to help users discover hidden patterns, link various location-centered data to the price, as well as explore, filter and compare the properties, in order to easily find their preferred properties. In particular, we make the following contributions: (1) We present a problem abstraction for designing visualizations that help home buyers analyse the real estate data. Specifically, our data abstraction integrates heterogeneous data from different channels into a location-centred integrated real estate dataset. (2) We propose an interactive visual analytic procedure to help less informed users gradually learn about the local real estate market, upon which users exploit this learned knowledge to specify their individual requirements in property seeking. (3) We propose a series of designs to visualize properties/suburbs in different dimensions and in different granularities. We have collected, integrated and cleaned last 10 year’s real estate sold records in Australia as well as their location-related education, facility and transportation profiles, to generate a real multi-dimensional data repository, and implemented a system prototype for public access ( http://115.146.89.158 ). At last, we present case studies based on real-world datasets and real scenario to demonstrate the usefulness and effectiveness of our system. [ABSTRACT FROM AUTHOR]

This work proposes a non stationary random field model to describe the spatial variability of housing prices that are affected by a localized externality. The model allows for the effect of the localized externality on house prices to be represented in the mean function and/or the covariance function of the random field. The correlation function of the proposed model is a mixture of an isotropic correlation function and a correlation function that depends on the distances between home sales and the localized externality. The model is fit using a Bayesian approach via a Markov chain Monte Carlo algorithm. A dataset of 437 single family home sales during 2001 in the city of Cedar Falls, Iowa, is used to illustrate the model. [ABSTRACT FROM AUTHOR]