scholarly journals Valuation methods for the housing market: Evidence from Budapest

2016 ◽  
Vol 66 (3) ◽  
pp. 527-546 ◽  
Author(s):  
Dávid Kutasi ◽  
Milán Csaba Badics
Keyword(s):  
Neural Network ◽  
Real Estate ◽  
Large Scale ◽  
Housing Prices ◽  
Market Price ◽  
Housing Price ◽  
Real Estate Market ◽  
Hedonic Regression ◽  
Valuation Methods ◽  
Valuation Method

Different valuation methods and determinants of housing prices in Budapest, Hungary are examined in this paper in order to describe price drivers by using an asking price dataset. The hedonic regression analysis and the valuation method of the artificial neural network are utilised and compared using both technical and spatial variables. In our analyses, we conclude that according to our sample from the Budapest real estate market, the Multi-Layer Preceptron (MLP) neural network is a better alternative for market price prediction than hedonic regression in all observed cases. To our knowledge, the estimation of housing price drivers based on a large-scale sample has never been explored before in Budapest or any other city in Hungary in detail; moreover, it is one of the first papers in this topic in the CEE region. The results of this paper lead to promising directions for the development of Hungarian real estate price statistics.

scholarly journals Prediction of China’s Housing Price Based on a Novel Grey Seasonal Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dayin Li ◽  
Lianyi Liu ◽  
Haitao Lv
Keyword(s):  
Real Estate ◽  
Prediction Models ◽  
Housing Prices ◽  
Market Price ◽  
Housing Price ◽  
Real Estate Market ◽  
Accurate Prediction ◽  
Price Changes ◽  
New Information ◽  
Seasonal Model

The fluctuation of real estate prices has an important impact on China's economic development. Accurate prediction of real estate market price changes has become the focus of scholars. The existing prediction methods not only have great limitations on the input variables but also have many deficiencies in the nonlinear prediction. In the process of real estate market price forecasting, the priority of data and the seasonal fluctuation of housing price are important influencing factors, which are not taken into account in the traditional model. In order to overcome these problems, a novel grey seasonal model is proposed to predict housing prices in China. The main method is to introduce seasonal factor decomposition into the new information priority grey prediction model. Two practical examples are used to test the performance of the new information priority grey seasonal model. The results show that compared with the existing prediction models, this method has better applicability and provides more accurate prediction results. Therefore, the proposed model can be a simple and effective tool for housing price prediction. At the same time, according to the prediction results, this paper analyzes the causes of housing price changes and puts forward targeted suggestions.

A Study of Influence upon Inflation Posed by Volatility of Housing Price

2016 ◽  
Vol 20 (4) ◽  
pp. 597-606
Author(s):  
Jun Pi ◽  
◽  
Yu Song ◽  
Shenggang Yang ◽  
Fang Ju ◽  
...  
Keyword(s):  
Real Estate ◽  
Asset Price ◽  
Housing Prices ◽  
Market Price ◽  
Housing Price ◽  
Real Estate Market ◽  
Economic Market ◽  
Causality Test ◽  
The Real Estate ◽  
Effect Theory

In recent 30 years, countries from the world have attached great attention to the influence on inflation posed by asset price. Real estate market is a very important economic market for any country. Therefore, housing price has become a hot topic for discussion and research in China. Whether housing price will affect a country’s inflation or not and to what extent the effect will be are social focuses. Hence, it is feasible to theoretically apply Tobin’s Q Theory in this paper, combine the real estate market price with the assets replacement and study the inflationary impact posed by the housing price, through the application of the wealth effect theory. By using monthly statistics of China’s real estate market and inflation from the year 2005 to 2014, this paper will conduct a theoretical and empirical research on the influence that housing price has on inflation with the adoption of dynamic analysis methods including Granger Causality Test, impulse response and variance decomposition. Furthermore, this paper is featured with systematic and complete empirical thinking and methodology, comprehensive data selection and distinctive research results associated with the relationship between housing prices and inflations. According to the study result, housing price is the Granger Cause of inflation and will not drive inflation in short time. But as time passes, this effect will be gradually enhanced. This paper suggests that housing price and other price factors should be taken into consideration so as to establish a broad-sense inflation index in China.

scholarly journals Did the COVID-19 Pandemic Crisis Affect Housing Prices Evenly in the U.S.?

2021 ◽  
Vol 13 (21) ◽  
pp. 12277
Author(s):  
Xinba Li ◽  
Chuanrong Zhang
Keyword(s):  
Real Estate ◽  
Spatial Patterns ◽  
Price Change ◽  
Housing Prices ◽  
Housing Price ◽  
Real Estate Market ◽  
Moran’S I ◽  
Moran's I ◽  
Price Changes ◽  
The U.S

While it is well-known that housing prices generally increased in the United States (U.S.) during the COVID-19 pandemic crisis, to the best of our knowledge, there has been no research conducted to understand the spatial patterns and heterogeneity of housing price changes in the U.S. real estate market during the crisis. There has been less attention on the consequences of this pandemic, in terms of the spatial distribution of housing price changes in the U.S. The objective of this study was to explore the spatial patterns and heterogeneous distribution of housing price change rates across different areas of the U.S. real estate market during the COVID-19 pandemic. We calculated the global Moran’s I, Anselin’s local Moran’s I, and Getis-Ord’s statistics of the housing price change rates in 2856 U.S. counties. The following two major findings were obtained: (1) The influence of the COVID-19 pandemic crisis on housing price change varied across space in the U.S. The patterns not only differed from metropolitan areas to rural areas, but also varied from one metropolitan area to another. (2) It seems that COVID-19 made Americans more cautious about buying property in densely populated urban downtowns that had higher levels of virus infection; therefore, it was found that during the COVID-19 pandemic year of 2020–2021, the housing price hot spots were typically located in more affordable suburbs, smaller cities, and areas away from high-cost, high-density urban downtowns. This study may be helpful for understanding the relationship between the COVID-19 pandemic and the real estate market, as well as human behaviors in response to the pandemic.

scholarly journals Identifying Real Estate Opportunities Using Machine Learning

2018 ◽  
Vol 8 (11) ◽  
pp. 2321 ◽  
Author(s):  
Alejandro Baldominos ◽  
Iván Blanco ◽  
Antonio Moreno ◽  
Rubén Iturrarte ◽  
Óscar Bernárdez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Housing Prices ◽  
Market Price ◽  
Real Estate Market ◽  
Support Vector ◽  
Regression Problem ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market

The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.

Artificial intelligence algorithms to predict Italian real estate market prices

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luca Rampini ◽  
Fulvio Re Cecconi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Real Estate ◽  
House Prices ◽  
Housing Prices ◽  
Real Estate Market ◽  
Content Type ◽  
Two Cities ◽  
Artificial Neural

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.

scholarly journals Public Space, Tourism and Mobility. Projects, Impacts and Tensions in Lisbon’s Urban Regeneration Dynamics

2019 ◽  
pp. 29-56
Author(s):  
João Rafael Santos
Keyword(s):  
Real Estate ◽  
Public Space ◽  
Large Scale ◽  
Central Government ◽  
Public Investment ◽  
Population Decline ◽  
Housing Prices ◽  
Urban Regeneration ◽  
Real Estate Market ◽  
The Impact

In the wake of severe economic slowdown during the 2008-2015 crisis, and despite continued constraints on public investment in large scale infrastructure, Lisbon is emerging as one of the most attractive destinations in Europe. Tourism has been driving major spatial, functional and social changes, initially in the city’s historical districts, and nowadays exerts impact across a much larger urban and regional area. Tourism, together with new drivers of the real-estate market, is promoting the renovation of formerly vacant or rundown built stock, taking advantage of a rather fragile socio-economic milieu and changing the face of residential, commercial and public space landscapes. Recently upgraded transportation nodes and extensive improvements on public space have also played a meaningful role in this process. Central government and municipality rationale have underpinned its role in providing accessibility, “attractivity”, and “heritage valorisation”, aiming to attract young residents after decades of resident population decline. In contrast to considerable public investment in public space and infrastructure, very limited funding or policy has been targeted at maintaining an affordable housing and real-estate market: thus leaving much of the public investment return to the private sector. Criticism of gentrification and “touristification”, rising housing prices, and pressure on infrastructure is growing accordingly. The paper provides insight into aspects of this process, with a focus on the relational aspects of mobility upgrade, public space renewal and inner-city urban regeneration. Several urban projects are mapped and broadly characterised in their spatial and functional relationship with tourism. An interpretative framework that combines them with the forms of territorialisation and the main conflicts and tensions is offered as a contribution to the ongoing discussion. Conclusions point to the complex and powerful role that public space and mobility infrastructure play in the impact of territorialising tourism: as supports for better qualified, multi-scalar and shared urban spaces and as drivers of a more balanced, diverse and socially-inclusive urban tourism development.

scholarly journals GIS-Based Spatial Autocorrelation Analysis of Housing Prices Oriented towards a View of Spatiotemporal Homogeneity and Nonstationarity: A Case Study of Guangzhou, China

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Shaopei Chen ◽  
Dachang Zhuang ◽  
Huixia Zhang
Keyword(s):  
Real Estate ◽  
Spatial Autocorrelation ◽  
Large Scale ◽  
Housing Prices ◽  
Geographical Location ◽  
Housing Price ◽  
Housing Markets ◽  
Linear Regression Models ◽  
Service Intensity ◽  
Spatial Homogeneity

In the past decades, the booming growth of housing markets in China triggers the urgent need to explore how the rapid urban spatial expansion, large-scale urban infrastructural development, and fast-changing urban planning determine the housing price changes and spatial differentiation. It is of great significance to promote the existing governing policy and mechanism of housing market and the reform of real-estate system. At the level of city, an empirical analysis is implemented with the traditional econometric models of regressive analysis and GIS-based spatial autocorrelation models, focusing in examining and characterizing the spatial homogeneity and nonstationarity of housing prices in Guangzhou, China. There are 141 neigborhoods in Guangzhou identified as the independent individuals (named as area units), and their values of the average annual housing prices (AAHP) in (2009–2015) are clarified as the dependent variables in regressing analysis models used in this paper. Simultaneously, the factors including geographical location, transportation accessibility, commercial service intensity, and public service intensity are identified as independent variables in the context of urban development and planning. The integration and comparative analysis of multiple linear regression models, spatial autocorrelation models, and geographically weighted regressing (GWR) models are implemented, focusing on exploring the influencing factors of house prices, especially characterizing the spatial heterogeneity and nonstationarity of housing prices oriented towards the spatial differences of urban spatial development, infrastructure layout, land use, and planning. This has the potential to enrich the current approaches to the complex quantitative analysis modelling of housing prices. Particularly, it is favorable to examine and characterize what and how to determine the spatial homogeneity and nonstationarity of housing prices oriented towards a microscale geospatial perspective. Therefore, this study should be significant to drive essential changes to develop a more efficient, sustainable, and competitive real-estate system at the level of city, especially for the emerging and dynamic housing markets in the megacities in China.

The impact of external factors on housing prices in Lithuania

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alina Stundziene ◽  
Vaida Pilinkienė ◽  
Andrius Grybauskas
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Housing Price ◽  
Real Estate Market ◽  
External Factors ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
The Impact

Purpose This paper aims to identify the external factors that have the greatest impact on housing prices in Lithuania. Design/methodology/approach The econometric analysis includes stationarity test, Granger causality test, correlation analysis, linear and non-linear regression modes, threshold regression and autoregressive distributed lag models. The analysis is performed based on 137 external factors that can be grouped into macroeconomic, business, financial, real estate market, labour market indicators and expectations. Findings The research reveals that housing price largely depends on macroeconomic indicators such as gross domestic product growth and consumer spending. Cash and deposits of households are the most important indicators from the group of financial indicators. The impact of financial, business and labour market indicators on housing price varies depending on the stage of the economic cycle. Practical implications Real estate market experts and policymakers can monitor the changes in external factors that have been identified as key indicators of housing prices. Based on that, they can prepare for the changes in the real estate market better and take the necessary decisions in a timely manner, if necessary. Originality/value This study considerably adds to the existing literature by providing a better understanding of external factors that affect the housing price in Lithuania and let predict the changes in the real estate market. It is beneficial for policymakers as it lets them choose reasonable decisions aiming to stabilize the real estate market.

scholarly journals Identifying Real Estate Opportunities Using Machine Learning

2018 ◽  
Author(s):  
Alejandro Baldominos ◽  
Antonio José Moreno ◽  
Rubén Iturrarte ◽  
Óscar Bernárdez ◽  
Carlos Afonso
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Housing Prices ◽  
Market Price ◽  
Real Estate Market ◽  
Support Vector ◽  
Regression Problem ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market

The real estate market is exposed to many fluctuations in prices, because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem, that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, $k$-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.

scholarly journals An Innovative GIS-Based Territorial Information Tool for the Evaluation of Corporate Properties: An Application to the Italian Context

2020 ◽  
Vol 12 (14) ◽  
pp. 5836 ◽  
Author(s):  
Marco Locurcio ◽  
Pierluigi Morano ◽  
Francesco Tajani ◽  
Felicia Di Liddo
Keyword(s):  
Real Estate ◽  
Housing Price ◽  
Real Estate Market ◽  
The Real Estate ◽  
Valuation Methods ◽  
Price Bubble ◽  
Proposed Model ◽  
Italian Context ◽  
Automated Valuation ◽  
Credit Institutions

The financial transmission of the USA’s housing price bubble has highlighted the inadequacy of the valuation methods adopted by the credit institutions, due to their static nature and inability to understand complex socio-economic dynamics and their related effects on the real estate market. The present research deals with the current issue of using Automated Valuation Methods for expeditious assessments in order to monitor and forecast market evolutions in the short and medium term. The paper aims to propose an evaluative model for the corporate market segment, in order to support the investors’, the credit institutions’ and the public entities’ decision processes. The application of the proposed model to the corporate real estate segment market of the cities of Rome and Milan (Italy) outlines the potentialities of this approach in property big data management. The elaboration of input and output data in the GIS (Geographic Information System) environment allowed the development of an intuitive platform for the immediate representation of the results and their easy interpretation, even to non-expert users.

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