Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we define and capture a fine-grained location profile powered by a diverse range of location data sources, such as transportation profile (e.g., distance to nearest train station), education profile (e.g., school zones and ranking), suburb profile based on census data, facility profile (e.g., nearby hospitals, supermarkets). Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently. However, such modeling ignores the relatedness between partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and each partition obtained is aligned with a task. Furthermore, we select specific MTL-based methods with different regularization terms to capture and exploit the relatedness between tasks. Based on real-world house transaction data collected in Melbourne, Australia. We design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.
House is usually treated as a heterogeneous goods, defined by a bundle of utility bearing features @cite_33 @cite_21 . Therefore, the house price can be considered as a quantitative representation of a set of these features. Over the past decades, a large amount of studies have examined the relationship between house price and house features. For example, Kr 'o l @cite_32 investigated the relationship between the price of an apartment and its significant features based on the results of hedonic analysis in Poland. The work of @cite_51 discussed which house features have negative or positive effects upon the value of the house in Turkey. Kryvobokov and Wilhelmsson @cite_40 derived the weights of the relative importance of location features that influence the market values of apartments in Donetsk, Ukraine. @cite_14 compared measures of location using both distances and travel time, to the CBD, and to multiple employment centers to understand how residence location relative to employment location affects house price in Indianapolis, Indiana, USA. Ozalp and Akinci @cite_19 determined the housing and environmental features that were effective on residential real estate sale prices in Artvin, Turkey.
@cite_14 @cite_33 @cite_21 @cite_32 @cite_19 @cite_40 @cite_51
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A measure of location relative to employment is often included in hedonic housing price models. This is most often distance to the center, based on the monocentric model, which does not consider the decentralization of employment in urban areas. This paper tests the perfor-mance of alternative measures of location, considering both distance and time to the center and to multiple employment centers and measures of accessibility to employment and change in accessibility. The measures using multiple employment centers and accessibility perform better than simple distance to the center, with the combination of accessibility to employment and change in accessibility doing best. 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. 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. 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. 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. In this study, there has been aimed to determine the factors that affect the price of flats in the housing sector in Turkey with a hedonic pricing model. According to the model results, the house’s having residential swimming pool, a jacuzzi and a water tank, its being a duplex, its central heating system, its being closer to the center, the size of the house, the bathroom floor’s being vinyl or PVC, being closer to banking services and compulsory education services, its having cable TV, telephone lines and parking opportunities increases the value of the house. The house is in the basement or ground floor of the building construction date is first, the room is the floor of ceramic tiles, bathroom floor screed (concrete road) the fuel used is coal, reduces the value of the house.