Housing Price Forecasting Research Articles

Enhancing House Price Predictability: A Comprehensive Analysis of Machine Learning Techniques for Real Estate and Policy Decision-Making

Accurate house price prediction is crucial for stakeholders in real estate markets and economic policy formulation. This research investigates the application of sophisticated machine learning (ML) algorithms to improve the precision of house price forecasting. By analyzing existing literature, we explore the methodologies employed in house price prediction using ML approaches. We emphasize the significance of precise predictions for various stakeholders, including homebuyers, sellers, investors, and policymakers. Additionally, this abstract critically evaluates the strengths and limitations of different ML techniques in predicting housing prices Our goal is to enhance predictability of models through rigorous analysis, thus facilitating informed decision-making when it comes to housing transactions, investments, and policy implementations through our research.

House price prediction using machine learning

The role of the real estate industry in economic development and social progress reflects the economic well-being of individuals and regions. With the increase of people's income level, the demand for housing is also increasing. Therefore, making a more accurate house price forecast will help people make the most correct strategy to buy a house when they need it. This study focuses on house price prediction in King County, Washington, a diverse real estate market. Leveraging machine learning models such as linear regression, random forest, neural networks and XGBoost, these supervised learning models are used to delve into house price forecasting. This research includes random forest and XGBoost, are implemented using Scikit-Learn tools. Besides, the Feedforward Neural Network is introduced with the drop out layer in order to reduce the occurrence of model fitting situations. The findings reveal that XGBoost achieves the highest accuracy, making it well-suited for precise price predictions. Additionally, the research identifies grade, sqft_living, and latitude as the three most influential features significantly affecting house prices within the dataset.

Housing Price Prediction Model and Impact Factors Analysis

Housing price forecast is usually used in macroeconomic regulation, which can effectively avoid the housing price explosion brought by economic growth and promote better housing market control. Considering the rapid growth of housing prices in China in the past decade, the prediction and analysis of housing prices have become a top priority. In this paper, we establish the VAR model to understand the rule of housing prices between different cities and reveal some potential factors affecting housing prices through variance decomposition and impulse response. The results of the experiment show that the factors affecting the change in housing prices are different for different regions, but it is undeniable that housing prices are often affected by the prices of previous years. These results will effectively assess the housing market in these cities and help the government make decisions.

Russian housing price forecast based on BP neural Network

Contemporarily, Russia's real estate market has huge potential for investors. The purpose of this paper is to investigated the impact of various factors on the real estate market price based on various statistical models. We collected past housing price data from the Internet. Subsequently, PCA model was adopted to reduce the dimension of the data to simplify the calculation. Finally, the BP model is implemented to predict the price changes in the real estate market. After calculation, our forecast data and the reality of housing prices are slightly different, but the forecast of the real estate market is consistent with the reality. In order to further find the influence of various factors on housing price, we also calculated the characteristic importance of different factors and found the most important influencing factor, i.e., the region. These results can provide investors with an idea to analyze the real estate market prices, and help policy makers find the real estate market prices in line with the market rules to avoid the occurrence of bubbles.

Model For Predicting London House Prices

With the growth of population, housing prices have become one of the important indicators to reflect the economic performance. Forecasting prices around the world will be part of social and economic development. Through the analysis of various factors affecting the market housing price, it is helpful to make a more accurate assessment of the future housing price trend. In a broad sense, accurate housing price forecast is helpful to the country's macro-control of the market housing price trend, and the prediction of future housing price is part of the strategic planning of enterprises. For consumers, the housing price forecast has a positive effect on the rational planning of individual economy. Since house prices are related to many factors, and there is a linear relationship between house prices and some factors that affect house prices, there are many linear graphs to study this problem. The research direction of this paper is to obtain data from Kaggle and conduct exploratory analysis on these data sets. The main method used in this article is linear regression, and no other forecasting methods will be added. What this work needs to do is use the right machine learning methods to predict home values to create a complex property model under generally stable conditions, which is this work aims to do in this study. Through many data sets show that the multiple linear regression model does achieve a relatively excellent housing price prediction effect.

RHPMF: A context-aware matrix factorization approach for understanding regional real estate market

The real estate market has a significant impact on people’s daily life. Therefore, it is crucial to understand the real estate market from both spatial and temporal perspectives, while there is still a lack of research in real estate industries. In this paper, a regional house price mining and forecasting (RHPMF) framework is proposed to help people intuitively understand the spatial distribution and temporal evolution of the urban estate market based on real-world housing data and urban contexts such as demographics and criminal records. Specifically, the RHPMF framework introduces a context-aware matrix factorization to extract crucial spatial and temporal price factors for revealing the housing market. Meanwhile, the RHPMF can forecast future regional house prices by manipulating the two price factors. Consequently, this study presents extensive exploratory analysis and experiments in Virginia Beach, Philadelphia, and Los Angeles to verify the proposed RHPMF. These case studies indicate that the RHPMF framework can accurately capture the market’s spatial distribution and temporal evolution and forecast future regional house prices compared with recent baselines. The experimental results suggest the great potential of the proposed RHPMF for applications in real estate industries.

House Price Forecasting in Ames Based on Bayesian Regularized BP Neural Network

Housing has always been an important issue related to the national economy and people's livelihood. House prices not only affect people's welfare, but also have a significant impact on the national economy and social stability. Therefore, the prediction of house prices is also a necessary means. This paper forecasts the housing price data of Ames City in the United States, and establishes a Bayesian regularized BP neural network model to solve the nonlinear mapping relationship between housing prices and indicators. Through curve fitting analysis of samples, the overall R value is 0.9667, the overall result is better, and the error histogram also conforms to normal distribution. The experimental results show that the BP neural network model based on Bayesian regularization is very effective in dealing with the problem of house price prediction, and can better analyze and predict the trend of house price. This study provides a basis for real estate developers to develop and position products, and also provides model support for buyers to better judge the real price of houses.

BP Network Model Based on SCLBOA for House Price Forecasting.

Butterfly optimization algorithm (BOA) is a new swarm intelligence algorithm mimicking the behaviors of butterflies. However, there is still much room for improvement. In order to enhance the convergence speed and accuracy of the BOA, we present an improved algorithm SCLBOA based on SIBOA, which incorporates a logical mapping and a Lévy flight mechanism. The logical chaotic map is used for population initialization, and then the Lévy flight mechanism is integrated into the SCLBOA algorithm. To evaluate the performance of the SCLBOA, we conducted many experiments on standard test functions. The simulation results suggest that the SCLBOA is capable of high-precision optimization, fast convergence, and effective global optimization, all of which show that our method outperforms other methods in solving mathematical optimization problems. Finally, the BP network is optimized according to the SCLBOA (SCLBOA-BP) to further verify the availability of the algorithm. Simulation experiments prove the practicability of this method by building a Boston housing price prediction model for training.

An Intelligent Homogeneous Model Based on an Enhanced Weighted Kernel Self-Organizing Map for Forecasting House Prices

Accurately forecasting housing prices will enable investors to attain profits, and it can provide information to stakeholders that housing prices in the community are falling, stabilizing, or rising. Previous studies on housing price forecasting mostly used hedonic pricing and weighted regression methods, which led to the lack of consideration of the nonlinear relationship model and its explanatory power. Furthermore, the attribute data of housing price forecasts are a heterogeneous study, and they are difficult to forecast accurately. Therefore, this study proposes an intelligent homogeneous model based on an enhanced weighted kernel self-organizing map (EW-KSOM) for forecasting house prices; that is, this study proposes an EW-KSOM algorithm to cluster the collected data and then applies random forest, extra tree, multilayer perception, and support vector regression to forecast the house prices of full, district, and apartment complex data. In the experimental comparison, we compare the performance of the proposed enhanced weighted kernel self-organizing map with the listing clustering methods. The results show that the best forecast algorithm is the combined EW-KSOM and random forest under the root mean square error and root-relative square error, and the proposed method can effectively improve the forecast capability of housing prices and understand the influencing factors of housing prices in full and important districts. Furthermore, we obtain that the top five key factors influencing house prices are transferred land area, house age, building transfer total area, population percentage, and the total number of floors. Lastly, the research results can provide references for investors and related organizations.

A Model Average Algorithm for Housing Price Forecast with Evaluation Interpretation

A Model Average Algorithm for Housing Price Forecast with Evaluation Interpretation

Artificial intelligence algorithms to predict Italian real estate market prices

PurposeThe assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.Design/methodology/approachAn extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.FindingsThe models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).Research limitations/implicationsAll the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.Practical implicationsThe accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.Originality/valueTo date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.

Second-hand house price index forecasting with neural networks

ABSTRACT The house market in China has been growing rapidly for the past decade and price forecasting has become a significant issue to the people and policymakers. We approach this problem by examining neural networks for second-hand house price index forecasting from 10 major cities for March 2012–May 2020. Our purpose is to build simple and accurate neural networks to contribute to pure technical house price forecasting of the Chinese market. We explore various model settings across the algorithm, delay, hidden neuron, and data spitting ratio, and arrive at a rather simple neural network with three delays and eight hidden neurons, which leads to stable performance of 0.8% average relative root-mean-square error across the 10 cities for the training, validation, and testing phases. Results here can be used on a standalone basis or combined with fundamental forecasting in forming perspectives of house price trends and conducting policy analysis.

House price forecasting with neural networks

• We develop neural network models for house price forecasting in China. • We explore the forecasting issue for 100 major Chinese cities. • We construct simple, accurate, and stable models. • The models are useful as technical tools and for policy analysis. The house market has been rapidly growing for the past decade in China, making price forecasting an important issue to the people and policy makers. We approach this problem by exploring neural networks for forecasting of house prices from one hundred major cities for the period of June 2010–May 2019, serving as the first study with such wide coverage for the emerging Chinese market through a machine learning technique. We aim at constructing simple and accurate neural networks as a contribution to pure technical forecasting of house prices. To facilitate the analysis, we investigate different model settings over the algorithm (the Levenberg-Marquardt, scaled conjugate gradient, and Bayesian regularization), delay (from two to six), hidden neuron (two, three, five, and eight), and data spitting ratio (70%–15%–15%, 60%–20%–20%, and 80%–10%–10% for trainingvalidationtesting), and arrive at a rather simple neural network with only four delays and three hidden neurons that leads to stable performance of 1% average relative root mean square error across the one hundred cities for the training, validation, and testing phases. We demonstrate the usefulness of the machine learning approach to the house price forecasting problem in the Chinese market. Our results could be used on a standalone basis or combined with fundamental forecasting in forming perspectives of house price trends and conducting policy analysis. Our empirical framework should not be difficult to deploy, which is an important consideration to many decision makers, and has potential to be generalized for house price forecasting of other cities in China.

ENHANCING THE ACCURACY OF MALAYSIAN HOUSE PRICE FORECASTING: A COMPARATIVE ANALYSIS ON THE FORECASTING PERFORMANCE BETWEEN THE HEDONIC PRICE MODEL AND ARTIFICIAL NEURAL NETWORK MODEL

The Hedonic Price Model (HPM), a prominent model used in real estate appraisal and economics, has been argued to be marred with nonlinearity, multicollinearity and heteroscedasticity problems that affect the accuracy of price predictions. An alternative method called Artificial Neural Network Model (ANN) was identified as capable of addressing the shortcomings of HPM and produces superior predictive performance. Hence, this study aims to evaluate the forecasting performance between HPM and ANN using Malaysian housing transaction data from the period between 2009 to 2018, sourced from the Valuation and Property Service Department, Johor Bahru. The models’ performance was evaluated and compared based on their statistical and predictive performance. Results showed that ANN outperformed HPM in both statistical and predictive performance. This study benefits the expansion of academic and practical knowledge in enhancing the accuracy of house price forecasting.

Housing Price Forecasting in Selected Polish Cities During the COVID-19 Pandemic

The COVID-19 pandemic represents a combined supply and demand shock to the financial and housing market but also an unusual negative shock in terms of the health of society (households) and national economy. The fall in housing demand was initially assumed together with price decreases as a consequence of the uncertainty of the health of society, significant falls in stock markets and corporate solvency. However, the results of research in selected Polish cities do not indicate such a significant market recession. This article examines the housing price dynamics and forecasting in Polish cities during the COVID-19 pandemic. The TRAMO/SEATS and ARIMA models were used for the decomposition and forecasting of dwelling time series. The Polish housing market, represented by selected local housing markets, still shows a growing trend despite the COVID-19 pandemic throughout 2020. The housing market may slow down in 2021, but the strong forecasted growth trends in Warszawa and Poznań suggest that there will be no significant price decline in Poland in the near future.

Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

The forecast of the real estate market is an important part of studying the Chinese economic market. Most existing methods have strict requirements on input variables and are complex in parameter estimation. To obtain better prediction results, a modified Holt's exponential smoothing (MHES) method was proposed to predict the housing price by using historical data. Unlike the traditional exponential smoothing models, MHES sets different weights on historical data and the smoothing parameters depend on the sample size. Meanwhile, the proposed MHES incorporates the whale optimization algorithm (WOA) to obtain the optimal parameters. Housing price data from Kunming, Changchun, Xuzhou and Handan were used to test the performance of the model. The housing prices results of four cities indicate that the proposed method has a smaller prediction error and shorter computation time than that of other traditional models. Therefore, WOA-MHES can be applied efficiently to housing price forecasting and can be a reliable tool for market investors and policy makers.

A Comprehensive Spatiotemporal Framework for Hedonic Pricing: Integrating the Comparable Sales Approach and Minimizing Spatial Omitted Variable Bias

This paper develops a theoretical and methodological framework that integrates Hedonic Pricing (HP), grid comparable sales approach (CSA), and nearest neighbors into a general spatiotemporal specification. By explicitly providing a theoretical justification for introducing spatial (or spatiotemporal) econometrics to HP, this approach is not only relevant to house price forecasting and automated valuation models (AVM) but also to valuing environmental goods capitalized in housing and to all other fields employing house pricing models. The resulting econometric CSA and spatiotemporal Durbin models provide higher prediction accuracy and reliability to alternatives by reducing the spatially-delineated omitted variable bias (OVB) common in HP. Spatiotemporal autoregressive and error models are also derived, providing specific conditions, under which their application can be justified. Our analysis reinforces the common real estate practice of selecting a small number of comparables in grid CSA and challenges AVM approaches, in which hundreds or thousands of comparables are introduced.

Local Affordability, House Price Forecasting, and Vulnerabilities

I find that high home-buying costs for a typical household, relative to a cumulative city-level average, help to predict downward pressure on future real house price growth at a 1-year horizon for Canadian cities over the 1980q1 to 2016q2 sample period. This insight is relevant for broader household vulnerabilities because of their interdependence with housing market conditions --- this interdependence is illustrated, for instance, by how a decline in house prices Granger-causes an increase in mortgage arrears. Based on ease of interpretability, relevant historical context, and in- and out-of-sample performance, local housing cost (i.e. affordability) measures may have some advantages for the monitoring of household sector vulnerabilities, versus other common measures such as debt-to-income.

Fintech and Network Economics in Real Estate: REWs and MLS As Policy Transmission Mechanisms, ‘Portfolio Screens’ and ‘Portfolio Frames’

Multiple Listing Systems (MLS), Real Estate Website (REW) Laws, and Rent-Control and Rent-Stabilization statutes (RCRS) are un-constitutional and could have affected the transmission of monetary policy and fiscal policies, and the rapid changes in housing prices and housing demand which occurred in the US and other countries between 1995 and 2010. MLS and REWs are fintech systems that are used around the world. There is an increasing and symbiotic relationship between the un-constitutionality and anti-competition effects of MLS, Real Estate Websites (REWs) and RCRS on one hand, and systemic risk and financial stability. MLS, Real Estate Websites (REWs) and RCRS can have substantial effects on consumer behavior in credit and asset markets – and this can precipitate structural changes in the financial services, real estate and retailing industries. Hence, all existing housing-demand models and housing price forecast models are grossly mis-specified primarily because they don’t incorporate legal factors and associated economic effects.

Multi-model Combination Housing Price Forecasting Based on Web Search Data

In the background of large data, taking the housing price in Xi'an as an example, this paper extracts the relevant search words data from Baidu Index and builds the neural network, support vector machine and random forests model, according to the purchase information inquired by the consumers in Baidu. The gradient boosting decision tree model is established by forecast results of the three single model to achieve a combination of housing prices forecast to determine the final residential sales price index forecast. The result shows that the fitting degree of the combined model is 0.995, and the prediction accuracy is 10.4% higher than the optimal single forecasting model. This method can calculate the new commodity housing price index two weeks in advance than the National Bureau of Statistics. In future studies, this approach will be applied to many types of market forecasts to help companies and consumers make the best decisions.