House Price Estimation Research Articles

The Real Estate Price Prediction Based on Multivariate Linear Regression Models

The price of households had increased greatly as the demands in the market increased. Real estate had become the most popular method to resist inflation. Thus, more experts and scholars are working on the research in this area, including but not limited to find the future trends of housing prices and the possible bubble of housing prices. This paper will use the basic data regression model in Excel, powered by Microsoft, to find the factors that lead to the appreciation or depreciation of real estate, and three different methods of dealing with the data will be applied which are standardization, interception, and normal normalization. As a result of complex processes, normalization is the best way to deal with the data that was chosen from Kaggle. In the meantime, a linear correlation was done for checking the independency of each variable, and turned out to be successful. According to the analysis, an inverse relationship between the variables house age and distance nearest to the store was found, and the lower the house age is, the higher the price is, higher the house age is, lower the price is. The main aim of this paper is to investigate the relationship of the factors and their impact of them on the price of the house, was achieved and solved mathematically, and therefore, this research paper has found its goals. These results shed light on guiding further exploration of house price estimation.

A House Price Modeling Based on Clustering and Kriging: The Medellín Case

In this study, house prices are modeled using a mixed two-stage model for mass appraisal employing valuations of second-hand housing units conducted in Medellín, Colombia. In the first stage, submarkets of houses that share non-spatial attributes are created using clustering; in the second stage, the spatial dependency is incorporated into the house price estimation using kriging. The best results were obtained when the sample was divided into three submarkets using property area and age as the classification criterion and later applying a Matérn kriging model to submarket 1, a spherical kriging model to submarket 2, and a circular kriging model to submarket 3. These results may provide further guidance to enhance mass appraisal practice in other Latin American cities as well as potentially other cities in developing countries.

Housing Price Estimation with Deep Learning: A Case Study of Sakarya Turkey

Shelter is one of the most basic human needs. Besides housing needs, the housing market is also very important for investment. It is also a market where many people, such as engineers, architects, real estate agents make economic gain. When a house is bought for living in it, it is not desired to be changed for many years, and when it is bought for investment, it is a tool that requires good income. Therefore, the best decision should be made when buying a house, and it should be scrutinized. Correct estimation of house prices is very important for both buyers to make the right decision and for sellers to sell without a loss. There are many parameters for estimating house prices. In addition to variables such as the number of floors, location, and several bathrooms used in previous studies, economic factors (such as the price of bread, foreign currency price, new car price) and the housing loan interest rate of the banks were taken as inputs in this study. Sakarya province, where all parameters can be tested to make a more accurate determination, was chosen as the research area. A comparison of polynomial regression, random forest, and deep learning methods was made and it was concluded that the most accurate method was deep learning. At the same time, it was determined which parameters are more effective in house price estimation.

Assessing the value of user-generated images of urban surroundings for house price estimation

Determinants of housing prices are particularly significant for monitoring and understanding housing prices. Traditional variables are measured through official statistics or questionnaire surveys, which are labour intensive and time-consuming. New forms of data, such as point of interest or street view imagery, have been used to extract housing location and neighbourhood features, but they cannot capture how different individuals recognised and evaluated the properties nearby, which may also be relevant in the house price estimation. Therefore, this study investigates whether user-generated images may be used to monitor and understand housing prices and how they influence real estate values. Within this context, perceived scenes features are extracted and quantified to blend with commonly used determinants of housing prices. Two machine learning algorithms, random forest and gradient boosting machines, are utilised and deployed for integration with a typical housing price modelling-hedonic price model. By comparing the performance and interpretability of different models, the relative importance of features and how they influence the estimation power of the models is visualised and analysed. The findings suggest that random forest predictions perform the best and are interpretable, with geotagged Flickr images adding 4.6% to the model’s accuracy (R2) from 61.9% to 66.5%. Although user-generated images increase minor value in house price estimation, they may be used as a supplementary data source to capture perception features for house price estimation. This could help the restructuring and optimisation of residential areas in future regional construction, planning and development.

Estimation of housing prices in Ecuador using hedonic and geostatistical models: A comparative analysis

Estimation of housing prices in Ecuador using hedonic and geostatistical models: A comparative analysis

Comparing OLS based hedonic model and ANN in house price estimation using relative location

Comparing OLS based hedonic model and ANN in house price estimation using relative location

House Price Prediction using Machine learning and Image Processing

We demonstrate that these urban features can be recorded by street views and satellite image data and enhance the estimate of house prices. In order to estimate house prices in London, UK, we recommend a pipeline that uses a deep neural network model to automatically extract visual features from images. In calculating the house price model, we use typical housing characteristics, such as age, size, and accessibility, as well as visual features from Google Street View images and Bing aerial pictures. We see promising outcomes where learning to describe a neighborhood’s urban efficiency facilitates the estimation of house prices, even when generalizing to previously unseen London boroughs. We discuss the use of non-linear vs. linear approaches to combine these signals with traditional house pricing models and explain how the interpretability of linear models helps one to specifically derive the visual desirability of neighborhoods as proxy variables that are both of importance in their own right and can be used as inputs to other econometric methods. This is particularly useful as it can be extended elsewhere after the network has been trained with the training data, enabling us to produce vivid complex maps of the desirability of London streets.

House Price Estimation with Alternative Data and Machine Learning: A Case Study of Residential House Prices in Surrey, Canada

Increasing availability and acceptance of Alternative Data in recent years have created challenges and opportunities for experimental exploration from unorthodox sources. In the meantime, rising popularity of Machine Learning algorithms has probably accelerated interest for more data ingestion, at the cost of demand for higher compute power. In this study, we attempt to propose and apply an empirical framework for house value attribution that incorporates Macroeconomic, Neighborhood-specific, and property-specific data, compatible with spatially adjusted econometric approaches and ML algorithms. Our case analysis of all residential properties in the City of Surrey, Canada, uses Random Forest method with over 40 variables on all 3 levels. The empirical results show impressive modeling accuracy with 76% Adjusted R-square for continuous price estimation and 10% misclassification rate for market segment analysis. Across price spectrum of market segments, property interior size and Mortgage Rate seem to possess high importance for all sub-markets. Certain Neighborhood variables seem to exhibit more segment disparity than others, a sign of differential customer preferences. Take Safety and Mobility for example. While Safety seems to be ubiquitously important for all market segments, Mobility is attributed with more value almost exclusively for High-end. This case study shows strong potentials for further analysis and interpretations through Economic and Social lens.

Who demands peri-urban nature? A second stage hedonic house price estimation of household’s preference for peri-urban nature

We analyze housing markets in a suburb north of the Danish capital Copenhagen. There the concentration of affluent households decreases rapidly with distance to nature. This indicates systematic differences in preferences that are highly correlated with demographics. We assess if and to what extent this is the case by conducting a second-stage hedonic house price study, where we recover household-specific preferences for the availability of peri-urban nature. Preference parameters are identified locally through restrictions on household utility functions. We assess the relationship between demographic factors and household Willingness To Pay for nature availability across the Willingness To Pay distribution. The results of the analysis show that households paid on average 968 EUR per year for the level of peri-urban nature they have available from their home. In extreme cases, some households paid more than seven times the average amount.Willingness To Pay for peri-urban nature increases with income and wealth across the entire population. However, an increase in income will increase Willingness To Pay more for households at the lower end of the willingness to pay distribution, compared to the effect at the high end of the Willingness To Pay distribution. Furthermore, increases in education levels are related to higher willingness to pay in the middle of the willingness to pay distribution, whereas further education has less impact on households with high willingness to pay. Single parent status, car ownership, and other factors also affect Willingness To Pay for nature availability.Our study contributes to the discussion of the distributional aspects of environmental policies with results based on a revealed-preference method where households face a real and binding budget constraint. Our results show that the socio-economic distribution of changes in amenity values is a relevant factor to consider when evaluating policies that affect the provision of nature close to urban areas.

Using photographs and metadata to estimate house prices in South Korea

PurposeMost prior attempts at real estate valuation have focused on the use of metadata such as size and property age, neglecting the fact that the building workmanship in the construction of a house is also a key factor for the estimation of house prices. Building workmanship, such as exterior walls and floor tiling correspond to the visual attributes of a house, and it is difficult to capture and evaluate such attributes efficiently through classical models like regression analysis. Deep learning approach is taken in the valuation process to utilize this visual information.Design/methodology/approachThe authors propose a two-input neural network comprising a multilayer perceptron and a convolutional neural network that can utilize both metadata and the visual information from images of the front view of the house.FindingsThe authors applied the two-input neural network to Guri City in Gyeonggi Province, South Korea, as a case study and found that the accuracy of house price estimations can be improved by employing image information along with metadata.Originality/valueFew studies considered the impact of the building workmanship in the valuation process. The authors revealed that it is useful to use both photographs and metadata for enhancing the accuracy of house price estimation.

Take a Look Around

When an individual purchases a home, they simultaneously purchase its structural features, its accessibility to work, and the neighborhood amenities. Some amenities, such as air quality, are measurable while others, such as the prestige or the visual impression of a neighborhood, are difficult to quantify. Despite the well-known impacts intangible housing features have on house prices, limited attention has been given to systematically quantifying these difficult to measure amenities. Two issues have led to this neglect. Not only do few quantitative methods exist that can measure the urban environment, but that the collection of such data is both costly and subjective. We show that street image and satellite image data can capture these urban qualities and improve the estimation of house prices. We propose a pipeline that uses a deep neural network model to automatically extract visual features from images to estimate house prices in London, UK. We make use of traditional housing features such as age, size, and accessibility as well as visual features from Google Street View images and Bing aerial images in estimating the house price model. We find encouraging results where learning to characterize the urban quality of a neighborhood improves house price prediction, even when generalizing to previously unseen London boroughs. We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract proxy variables for visual desirability of neighborhoods that are both of interest in their own right, and could be used as inputs to other econometric methods. This is particularly valuable as once the network has been trained with the training data, it can be applied elsewhere, allowing us to generate vivid dense maps of the visual appeal of London streets.

A new machine learning approach to house price estimation

A new machine learning approach to house price estimation

Kernel-based geographically and temporally weighted autoregressive model for house price estimation.

Spatiotemporal nonstationarity and autocorrelation are two crucial points in modeling geographical data. Previous studies have demonstrated that geographically and temporally weighted autoregressive (GTWAR) model accounts for both spatiotemporal nonstationarity and autocorrelation simultaneously to estimate house prices. Therefore, this paper proposes a kernel-based GTWAR (KBGTWAR) model by incorporating the basic principle of support vector machine regression into spatially and temporally varying coefficients model. The efficacy of KBGTWAR model is demonstrated through a case study on housing prices in the city of Shenzhen, China, from year 2004 to 2008. Comparing the existing models, KBGTWAR model obtains the lowest value for the residual sum of squares (RSS) and the highest value for the coefficient of determination R2. Moreover, KBGTWAR model improves the goodness of fit of the existing GTWAR model from 12.0 to 4.5 in terms of RSS, from 0.914 to 0.968 in terms of R2 and from 3.84 to 4.45 in terms of F-statistic. The results show that KBGTWAR model provides a comparatively high goodness of fit and sufficient explanatory power for both spatiotemporal nonstationarity and autocorrelation. The results of this study demonstrate that the proposed KBGTWAR model can be used to effectively formulate polices for real estate management.

The Use of Arti fi cial Neural Networks (ANN) in Forecasting Housing Prices in Ankara, Turkey

The purpose of this paper is to forecast housing prices in Ankara, Turkey using the artifi cial neural networks (ANN) approach. The data set was collected from one of the biggest real estate web pages during April 2013. A three-layer (input layer – one hidden layer – output layer) neural network is designed with 15 different inputs to forecast the future housing prices. The proposed model has a success rate of 78%. The results of this paper would help property investors and real estate agents in developing more effective property pricing management in Ankara. We believe that the artifi cial neural networks (ANN) proposed here will serve as a reference for countries that develop artifi cial neural networks (ANN) method-based housing price determination in future. Applying the artifi cial neural networks (ANN) approach for estimation of housing prices is relatively new in the fi eld of housing economics. Moreover, this is the fi rst study that uses the artifi cial neural networks (ANN) approach for analyzing the housing market in Ankara/Turkey.

A Geographically Temporal Weighted Regression Approach with Travel Distance for House Price Estimation

Previous studies have demonstrated that non-Euclidean distance metrics can improve model fit in the geographically weighted regression (GWR) model. However, the GWR model often considers spatial nonstationarity and does not address variations in local temporal issues. Therefore, this paper explores a geographically temporal weighted regression (GTWR) approach that accounts for both spatial and temporal nonstationarity simultaneously to estimate house prices based on travel time distance metrics. Using house price data collected between 1980 and 2016, the house price response and explanatory variables are then modeled using both the GWR and the GTWR approaches. Comparing the GWR model with Euclidean and travel distance metrics, the GTWR model with travel distance obtains the highest value for the coefficient of determination ( R 2 ) and the lowest values for the Akaike information criterion (AIC). The results show that the GTWR model provides a relatively high goodness of fit and sufficient space-time explanatory power with non-Euclidean distance metrics. The results of this study can be used to formulate more effective policies for real estate management.

Can Property Taxes Reduce House Price Volatility? Evidence from U.S. Regions

We use a novel dataset on effective property tax rates in U.S. states and metropolitan statistical areas (MSAs) over the 2005–2014 period to analyze the relationship between property tax rates and house price volatility. We find that property tax rates have a negative impact on house price volatility. The impact is causal, with increases in property tax rates leading to a reduction in house price volatility. The results are robust to different measures of house price volatility, estimation methodologies, and additional controls for housing demand and supply. The outcomes of the analysis have important policy implications and suggest that property taxation could be used as an important tool to dampen house price volatility.

House selling price assessment using two different adaptive neuro-fuzzy techniques

Abstract It is of high importance to estimate the house selling prices because sellers and buyers need this information in an accurate way at time of sale. As a result of the fact that the estimation of the house prices is extorsive considerable research has been published in the literature on estimating house prices. This study covers the application of two different adaptive neuro-fuzzy (ANFIS) approaches for the estimation of house selling price. In this study, two different ANFIS models, namely ANFIS with grid partition (ANFIS-GP) and ANFIS with sub clustering (ANFIS-SC), were used in forecasting house prices, and the results were evaluated. Comparison of results from the two techniques indicated that the ANFIS-GP models performed better than the ANFIS-SC models. The study suggests that the ANFIS-GP technique can be successfully used in the estimation of house prices in the construction sector.

Remote Sensing-based House Value Estimation Using an Optimized Regional Regression Model

This study proposed a new method to predict residential property value using remote sensing data as a major data source substitute to traditional inputs in house price estimation models. An optimized regional regression (ORR) approach was proposed in this study. This approach integrated a differential evolution optimization algorithm along with the ordinary least square regression to improve house value prediction accuracy. In addition to ORR, four other regression methods, random forest, Cubist regression trees, geographically weighted regression, and global ordinary least square, were also employed to provide a comparison. Results showed that models using remote sensing data are capable of acquiring accurate house price information. In addition, the volume of residential buildings proved to be an efficient substitute for total living area, the most important variable of the house price estimation model (i.e., a hedonic model). The ORR approach yielded the most accurate predictions followed by the geographically weighted regression. Further investigation indicated that the ORR approach has three major advantages: it is effective, stable, and the results are readily interpretable.

Estimation of house prices in regions with small sample sizes

House price indexes had become important economic indicators worldwide, since movements in house prices have been closely correlated with the economic cycle. In order to compute these kind of indexes it is imperative to produce reliable estimates of the average transaction price of houses, not only at the macrolevel (e.g. national and state level), but also at the microlevel (e.g. district, municipalities or further disaggregate regional level). In Portugal, there is a rapidly growing demand of such microlevel statistics since the beginning of the recent financial and economic crisis. The Portuguese Statistical Office provides a range of invaluable data at national level; however, this data cannot be used directly to produce reliable regional-level estimates due to small sample sizes. In this paper we employ small area estimation techniques to produce design and model-based estimates of average transaction price of houses for Portuguese regions with small sample sizes. Our results show that the model-based estimates based on spatial and temporal models are more accurate than the traditional direct design-based estimates. The use of these techniques allows the production of information at disaggregated regional levels that would not be available under the traditional direct estimation approaches. Furthermore, it is even possible to produce reliable model-based estimates for geographical areas without sample. The estimates are expected to provide invaluable information to policy-analysts and decision-making.

A functional connectivity approach for modeling cross-sectional dependence with an application to the estimation of hedonic housing prices in Paris

This paper proposes a functional connectivity approach, inspired by brain imaging literature, to model cross-sectional dependence. Using a varying parameter framework, the model allows correlation patterns to arise from complex economic or social relations rather than being simply functions of economic or geographic distances between locations. It nests the conventional spatial and factor model approaches as special cases. A Bayesian Markov Chain Monte Carlo method implements this approach. A small scale Monte Carlo study is conducted to evaluate the performance of this approach in finite samples, which outperforms both a spatial model and a factor model. We apply the functional connectivity approach to estimate a hedonic housing price model for Paris using housing transactions over the period 1990–2003. It allows us to get more information about complex spatial connections and appears more suitable to capture the cross-sectional dependence than the conventional methods.