Many brokers have adapted their operation to exploit the potential of the web. Despite the importance of the real estate classifieds, there has been little work in analyzing such data. In this paper we propose a two-stage regression model that exploits the textual data in real estate classifieds. We show how our model can be used to predict the price of a real estate classified. We also show how our model can be used to highlight keywords that affect the price positively or negatively. To assess our contributions, we analyze four real world data sets, which we gathered from three different property websites. The analysis shows that our model (which exploits textual features) achieves significantly lower root mean squared error across the different data sets and against variety of regression models.
Due to the importance of valuating a real estate property, there has been extensive research on automatic valuation @cite_10 @cite_3 @cite_8 @cite_9 @cite_18 @cite_20 . Most of that work used hedonic models, which assumed the price can be predicted from a combination (usually linear) of the property structured features @cite_14 such as number of bedrooms and the location. Traditional hedonic models were based on human expertise, where the model parameters are usually hand-coded by experts, unlike our proposed method here, which uses data mining. There has been growing literature on the use of data mining techniques to analyze real estate data @cite_11 @cite_17 @cite_13 @cite_12 @cite_2 , however, most of the previous work focused only on structured features and ignored textual features. We review sample of these works in the remainder of this section.
@cite_13 @cite_18 @cite_14 @cite_8 @cite_9 @cite_17 @cite_3 @cite_2 @cite_10 @cite_12 @cite_20 @cite_11
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This paper concentrates on empirical and methodological issues of the application of econometric methods to modelling real estate market. The presented hedonic analysis of apartments’ prices in Wroclaw is based on the dataset consisting of over ten thousands offers from the secondary real estate market. The models estimated as the result of the research allow for pricing the apartments, as well as its characteristics.The foundations of hedonic methods are formed by the so-called hedonic hypothesis which states that heterogeneous commodities are characterized by a set of attributes relevant both from the point of view of the customer and the producer. As a consequence, the price of a commodity is determined as an aggregate of values estimated for each significant characteristic of this commodity. The hedonic model allows to price the commodity as well as to identify and estimate the prices of respective attributes, including the prices which are not directly observable on the market. The latter is particularly useful for the real estate market as it enables pricing location-related, neighbourhood-related and structure-related characteristics of housing whose values cannot be obtained otherwise. This paper introduces an integrative approach to hedonic house price modeling which utilizes high density 3D airborne laser scanning (ALS) data. In general, it is shown that extracting exploratory variables using 3D analysis – thus explicitly considering high-rise buildings, shadowing effects, etc. – is crucial in complex urban environments and is limited in well-established raster-based modeling. This is fundamental in large-scale urban analyses where essential determinants influencing real estate prices are constantly missing and are not accessible in official and mass appraiser databases. More specifically, the advantages of this methodology are demonstrated by means of a novel and economically important externality, namely incoming solar radiation, derived separately for each flat. Findings from an empirical case study in Vienna, Austria, applying a non-linear generalized additive hedonic model, suggest that solar radiation is significantly capitalized in flat prices. A model comparison clearly proves that the hedonic model accounting for ALS-based solar radiation performs significantly superior. Compared to a model without this externality, it increases the model’s explanatory power by approximately 13 and additionally reduces the prediction error by around 15 . The results provide strong evidence that explanatory variables originating from ALS, explicitly regarding the immediate 3D surroundings, enhance traditional hedonic models in urban environments. This paper presents the insights gained from applying data mining techniques, in particular neural networks for the purposes of developing an intelligent model used to predict real estate property values based on variety of factors. A dataset of over one thousand transactions in real estate properties was used. The dataset included 15 variables obtained from the multiple listing system (MLS) database and captured information on transactions taking place during a period of three years. The results from applying data mining techniques to predict real estate values are promising. Future plans and recommendations for further expanding the study are given. Ranking residential real estates based on investment values can provide decision making support for home buyers and thus plays an important role in estate marketplace. In this paper, we aim to develop methods for ranking estates based on investment values by mining users' opinions about estates from online user reviews and offline moving behaviors (e.g., Taxi traces, smart card transactions, check-ins). While a variety of features could be extracted from these data, these features are Interco related and redundant. Thus, selecting good features and integrating the feature selection into the fitting of a ranking model are essential. To this end, in this paper, we first strategically mine the fine-grained discrminative features from user reviews and moving behaviors, and then propose a probabilistic sparse pair wise ranking method for estates. Specifically, we first extract the explicit features from online user reviews which express users' opinions about point of interests (POIs) near an estate. We also mine the implicit features from offline moving behaviors from multiple perspectives (e.g., Direction, volume, velocity, heterogeneity, topic, popularity, etc.). Then we learn an estate ranking predictor by combining a pair wise ranking objective and a sparsity regularization in a unified probabilistic framework. And we develop an effective solution for the optimization problem. Finally, we conduct a comprehensive performance evaluation with real world estate related data, and the experimental results demonstrate the competitive performance of both features and the proposed model.