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. The hedonic-based regression approach has been utilised extensively to investigate th relationship between house prices and housing characteristics. However, this approach is subject t criticisms arising from potential problems relating to fundamental model assumptions an estimation such as the identification of supply and demand, market disequilibrium, the selectio of independent variables, the choice of functional form of hedonic equation and marke segmentation. This study introduces and utilises an alternative approach-the decision tre approach, which is an important statistical pattern recognition tool. Using the Singapore resal public housing market as a case study, the article demonstrates the usefulness of this techniqu in examining the relationship between house prices and housing characteristics, identifying th significant determinants of housing prices and predicting housing prices. The built tree show that homebuyers are more concerned about the basic housing characteristics of two- and three room flats or four-room flats such as floor area, model type and flat age. However, homebuyer of five-room flats pay more attention to floor level in addition to the basic housin characteristics. In addition, homebuyers of executive apartments are less concerned about basi quantitative characteristics and have higher housing consumption expectations and pay mor attention to 'quality' and service characteristics such as recreational facilities and the livin environment. It is traditionally a challenge for home buyers to understand, compare and contrast the investment values of real estates. While a number of estate appraisal methods have been developed to value real property, the performances of these methods have been limited by the traditional data sources for estate appraisal. However, with the development of new ways of collecting estate-related mobile data, there is a potential to leverage geographic dependencies of estates for enhancing estate appraisal. Indeed, the geographic dependencies of the value of an estate can be from the characteristics of its own neighborhood (individual), the values of its nearby estates (peer), and the prosperity of the affiliated latent business area (zone). To this end, in this paper, we propose a geographic method, named ClusRanking, for estate appraisal by leveraging the mutual enforcement of ranking and clustering power. ClusRanking is able to exploit geographic individual, peer, and zone dependencies in a probabilistic ranking model. Specifically, we first extract the geographic utility of estates from geography data, estimate the neighborhood popularity of estates by mining taxicab trajectory data, and model the influence of latent business areas via ClusRanking. Also, we use a linear model to fuse these three influential factors and predict estate investment values. Moreover, we simultaneously consider individual, peer and zone dependencies, and derive an estate-specific ranking likelihood as the objective function. Finally, we conduct a comprehensive evaluation with real-world estate related data, and the experimental results demonstrate the effectiveness of our method. A hedonic model is specified for asking prices for apartments in Donetsk (Ukraine). This model is used to determine statistically significant location attributes. These attributes can be used for land assessment in a city where data on the land market are lacking. Distance gradients for CBD accessibility are investigated in different geographical directions. Separate models are created for sub‐samples located inside and outside the city centre. A spatial weight matrix is used to detect spatial autocorrelation. The regression results are compared with the valuation of experts. Parametric specifications for hedonic price equations are estimated using a data set from Alameda and San Francisco Counties and are compared to estimates using a nonparametric technique called locally weighted regression, LWR. LWR permits flexible estimation of the hedonic's curvature at median attributes and is less sensitive than standard regression techniques to the influence of unusual observations. The technique also avoids imposing a single functional form across time and municipalities. The LWR estimates of municipality-specific hedonics are then used to obtain implicit prices for housing attributes and to derive municipality-specific price indices. The results of extensive diagnostic checks of our technique are also reported. Copyright American Real Estate and Urban Economics Association. Owning residential real estate is very important not only for satisfying the need for shelter—a fundamental human right—but also it is considered as a good investment. It is true that real estates are heterogeneous because of their various structural, positional characteristics, as well as their local conditions, all of which affect the value of real estates. These are among the reasons for undertaking studies to determine the degree of importance and the parameters affecting the value of residential real estate. By using one of the widely used methods, hedonic pricing method (HPM), this study seeks to determine the structural and environmental characteristics that are effective on residential real estate sale prices in Artvin city center. With the highest number of residential real estate sales in 2015, the Orta neighborhood, in the Central District of Artvin, was chosen as the study area. The relationship between 18 parameters were analyzed, on the basis of the actual sales value, acquired from the purchasers of 81 residential properties in this neighborhood and taking into account the structure of Artvin city. In this context, using the functional form of HPM, semi-logarithmic model was generated. When examining the model, it was determined that four out of the supposedly effective 18 parameters were statistically significant and they were able to explain 84 of the variations on price. Moreover, the parameters of floor area, age, distance to primary school, and distance to city center were effective on the sales prices in the semi-logarithmic model. In addition, the results revealed that the structural characteristics are more influential on real estate prices than the environmental and accessibility features within Orta neighborhood in Artvin. Mass appraisal is the systematic appraisal of groups of properties as of a given date using standardized procedures and statistical testing. Mass appraisal is commonly used to compute real estate tax. There are three traditional real estate valuation methods: the sales comparison approach, income approach, and the cost approach. Mass appraisal models are commonly based on the sales comparison approach. The ordinary least squares (OLS) linear regression is the classical method used to build models in this approach. The method is compared with computational intelligence approaches - support vector machine (SVM) regression, multilayer perceptron (MLP), and a committee of predictors in this paper. All the three predictors are used to build a weighted data-depended committee. A self-organizing map (SOM) generating clusters of value zones is used to obtain the data-dependent aggregation weights. The experimental investigations performed using data cordially provided by the Register center of Lithuania have shown very promising results. The performance of the computational intelligence-based techniques was considerably higher than that obtained using the official real estate models of the Register center. The performance of the committee using the weights based on zones obtained from the SOM was also higher than of that exploiting the real estate value zones provided by the Register center. This paper compares alternative methods for taking spatial dependence into account in house price prediction. We select hedonic methods that have been reported in the literature to perform relatively well in terms of ex-sample prediction accuracy. Because differences in performance may be due to differences in data, we compare the methods using a single data set. The estimation methods include simple OLS, a two-stage process incorporating nearest neighborsâ€™ residuals in the second stage, geostatistical, and trend surface models. These models take into account submarkets by adding dummy variables or by estimating separate equations for each submarket. Based on data for approximately 13,000 transactions from Louisville, Kentucky, we conclude that a geostatistical model with disaggregated submarket variables performs best. Even with the recent developments in Web search of answering queries from structured data, search engines are still limited to queries with an objective answer, such as EUROPEAN CAPITALS or WOODY ALLEN MOVIES. However, many queries are subjective, such as SAFE CITIES, or CUTE ANIMALS. The underlying knowledge bases of search engines do not contain answers to these queries because they do not have a ground truth. We describe the Surveyor system that mines the dominant opinion held by authors of Web content about whether a subjective property applies to a given entity. The evidence on which SURVEYOR relies is statements extracted from Web text that either support the property or claim its negation. The key challenge that SURVEYOR faces is that simply counting the number of positive and negative statements does not suffice, because there are multiple hidden biases with which content tends to be authored on the Web. SURVEYOR employs a probabilistic model of how content is authored on the Web. As one example, this model accounts for correlations between the subjective property and the frequency with which it is mentioned on the Web. The parameters of the model are specialized to each property and entity type. Surveyor was able to process a large Web snapshot within a few hours, resulting in opinions for over 4 billion entity-property combinations. We selected a subset of 500 entity-property combinations and compared our results to the dominant opinion of a large number of Amazon Mechanical Turk (AMT) workers. The predictions of Surveyor match the results from AMT in 77 of all cases (and 87 for test cases where inter-worker agreement is high), significantly outperforming competing approaches.