Real Estate Market Data Research Articles

Study of Influential Factors on Housing Prices by Using MLR

With the emergence of the 2019 pandemic, the global economy experienced a downturn, but the housing market quickly rebounded post-pandemic, heightening the anxiety surrounding home buying. Previous research predominantly employed regression models to forecast housing prices, but these studies largely focused on specific factors. Hence, this article aims to offer a more comprehensive viewpoint by exploring the impact of multiple independent variables on housing prices, particularly emphasizing the effects of the age of buildings, the surrounding environment, and architectural factors. The methodology used in this study is the Multiple Linear Regression (MLR) model. It analyzes the real estate market data in Taipei, meticulously constructing and validating multiple models. The findings reveal that the age of buildings and the distance to the nearest subway station negatively influence housing prices, whereas the number of convenience stores positively impacts them. Among these factors, the quantity of convenience stores exerts the most significant effect on housing prices. Overall, this article provides a novel perspective and tools for understanding and predicting housing prices, assisting real estate developers and buyers in making more informed decisions in the complex real estate market. This study highlights the multidimensional nature of real estate value and contributes to the sustainable development of the real estate market.

Predicting New York Housing Prices: A Machine Learning Approach Incorporating School, Living facilities and Real Estate Market Factors

This study aims to predict housing prices in New York by utilizing machine learning methods that incorporate factors such as schools, living facilities, and the real estate market. This paper collected extensive data, including metrics on school quality, assessments of living facility convenience, and real estate market data. Employing a regression-based machine learning algorithm, the study incorporated these factors into a predictive model. Through training and testing the model, this study discovered that school quantity and the convenience of living facilities significantly impact housing prices. The predictive model demonstrated good accuracy and predictive capability on the test set, validating the effectiveness of this approach. The findings of this study provide valuable insights for real estate market participants, policymakers, and investors to better understand and forecast housing price trends in New York.

Leveraging consumer behavior and macroeconomic factors to increase real estate investment potential

The Indonesian real estate market has shown promising development in recent years, attracting investors seeking lucrative opportunities. This research aims to increase the success rate of real estate investments in Indonesia by examining the connection between macroeconomic factors and consumer behavior. This study employs vector autoregression (VAR) models to investigate the influence of macroeconomic data on the Indonesian house price index (HPI). We intend to determine the primary determinants of the HPI by analyzing various macroeconomic indicators, including GDP growth, CPI, crude oil, interest rates, and the unemployment rate. In addition, the research investigates the role of consumer behavior in Indonesian real estate searches. Real estate developers and investors can gain valuable insight into the effect of consumer preferences, motivations, and decision-making processes on demand functions by understanding consumer preferences, incentives, and decision-making processes. A comprehensive analysis of historical data and econometric modeling techniques are employed to accomplish research objectives. The research is conducted using pertinent macroeconomic indicators, real estate market data, and consumer surveys.

The New Stage of State Real Estate Cadastre Valuation (in the Russian Federation)

Accurate real estate market data is crucial for decision-making by governments, businesses, and individuals. Without it, costs rise and development of investment and tax policies suffers. In Russia, the state real estate cadastre valuation is the main data source, but its credibility wanes due to errors and delayed updates. This article reviews the 20-year evolution of this system through four stages. The aim of this study is to analyse the problems and shortcomings in conducting cadastre valuation and property assessment in the Russian Federation, and to propose recommendations for improving this system. In the process of research, normative documents that served as an information base for achieving the set goal were analysed, and a comparative analysis of property tax assessment systems and tax policies of various countries was conducted. Based on the conducted analysis, the author proposes recommendations to enhance tax policy and real estate cadastre valuation in Russia. The recommendations involve adjustments, updated tax value criteria, considering diverse property value factors, separating cadastre valuation from mass appraisal, and accommodating variations in tax base calculation. Implementing these recommendations can contribute to addressing issues in cadastre valuation and create a fairer, more effective taxation system for land and property.

Do informed REIT market participants respond to property sector mispricing?

ABSTRACT Sector mispricing represents the deviation of current and long-run sector fundamentals indicating either over- or undervaluation. We focus on the response of informed market participants to property sector mispricing in the context of equity REITs. We argue that REIT market participants such as institutional REIT investors and analysts have an informational advantage due to their access to commercial real estate market data. As a result, they are expected to respond to property sector mispricing. Using a sample of 2,637 firm-quarters of pure play equity REITs over the period of 1993 to 2020, we find that sector mispricing indeed impacts the decision-making of informed REIT market participants. The more overvalued (undervalued) a property sector is, the more institutional investors behave as net sellers (buyers) for REITs with the respective property type specialisation in the next quarter. Similarly, property sector overvaluation (undervaluation) results in lower (higher) net buy recommendations by analysts for REITs in the respective sector in the next quarter. However, our results are driven by smaller REITs and REITs with higher growth options. The sensitivity of institutional REIT investors and analysts to property sector mispricing also varies across different states of trading and recommendations respectively.

House Price Prediction and Recommendation

Abstract: Determining how much a house will sell for in a city is still a challenging and time-consuming task. This article's goal is to make predictions about the coherence of non-housing prices. A crucial method to ease the challenging design is to use machine learning, which can intelligently optimize the best pipeline fit for a task or dataset. For individuals who will be residing in a home for an extended period of time but not permanently, it is essential to predict the selling price. Real estate forecasting is a crucial part of the industry. From historical real estate market data, the literature seeks to extract pertinent information. Land price bubbles grow as a result of real estate prices, which leads to macroeconomic instability. The government should look into the variables that drive up real estate prices so that it can use them as a guide to assist stabilize the area. There are many economic circumstances that are in play at the time also have an impact on the selling price of a home.

Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times

Machine learning algorithms are being used for multiple real-life applications and in research. As a consequence of digital technology, large structured and georeferenced datasets are now more widely available, facilitating the use of these algorithms to analyze and identify patterns, as well as to make predictions that help users in decision making. This research aims to identify the best machine learning algorithms to predict house prices, and to quantify the impact of the COVID-19 pandemic on house prices in a Spanish city. The methodology addresses the phases of data preparation, feature engineering, hyperparameter training and optimization, model evaluation and selection, and finally model interpretation. Ensemble learning algorithms based on boosting (Gradient Boosting Regressor, Extreme Gradient Boosting, and Light Gradient Boosting Machine) and bagging (random forest and extra-trees regressor) are used and compared with a linear regression model. A case study is developed with georeferenced microdata of the real estate market in Alicante (Spain), before and after the pandemic declaration derived from COVID-19, together with information from other complementary sources such as the cadastre, socio-demographic and economic indicators, and satellite images. The results show that machine learning algorithms perform better than traditional linear models because they are better adapted to the nonlinearities of complex data such as real estate market data. Algorithms based on bagging show overfitting problems (random forest and extra-trees regressor) and those based on boosting have better performance and lower overfitting. This research contributes to the literature on the Spanish real estate market by being one of the first studies to use machine learning and microdata to explore the incidence of the COVID-19 pandemic on house prices.

Using Real-Time Dashboards to Monitor the Impact of Disruptive Events on Real Estate Market. Case of COVID-19 Pandemic in Australia

Real estate markets are complex both in terms of structure and dynamics: they are both influenced by and influence almost all aspects of the economy and are equally vulnerable to the shocks experienced by the broader economy. Therefore, understanding the extent and nature of the impact of large-scale disruptive events such as natural disasters and economic financial downturns on the real estate market is crucial to policy makers and market stakeholders. In addition to anticipating and preparing for long-term effects, it has become imperative for stakeholders to monitor and manage the short-term effects as well due to the emergence of ‘PropTech’ and ‘platform real estate’. In this work, we explore the use of online, real-time dashboards which have been used extensively in the context of urban management, policymaking, citizen engagement and disaster response as an appropriate tool for the purpose of monitoring real estate markets. We describe the process of designing, building, and maintaining an operational dashboard for monitoring the residential real estate market in Australia during the COVID-19 pandemic in 2020. We detail the techniques and methods used in creating the dashboard and critically evaluate their feasibility and usefulness. Finally, we identify the major challenges in the process, such as the spatial and temporal availability and veracity of the real estate market data, and we identify possible avenues for consistent, high-quality data; methodology; and outputs for further research.

Postopek modeliranja v okviru množičnega vrednotenja nepremičnin

The mass valuation of real estate in Slovenia is based on valuation models, which are made based on realised real estate market data and illustrate the functioning of the real estate market. The system is based on valuation models that simulate the behaviour of the real estate market and allow a statistically reliable assessment of market value. Models are formed using statistical methods of processing real estate market data. This paper presents the quality verification process and preparation of real estate market data. These data are used to design evaluation models or modelling, which consists of time adjustment of prices or rents, zoning, levelling, calibration, and analysis of quality parameters. Finally, the critical information of the modelling process made in the context of real estate mass valuation in Slovenia is summarised.

Which came first, the gentrification or the Airbnb? Identifying spatial patterns of neighbourhood change using Airbnb data

Over time, neighbourhoods experience different types of change. Neighbourhood change is a spatiotemporal process that involves analysing how attributes of a location change over time. In this article, we explore the capability of short-term property rental data to identify neighbourhoods where change is occurring. The article focuses on the relationship between data from the Airbnb platform and gentrification. Through a study in Dublin, Ireland, Airbnb data was analysed using state-of-the-art Emerging Hotspot Analysis to identify locations with high potential to experience gentrification and areas that have undergone recent development. The locations identified by the method are correlated with gentrification and neighbourhood change described in other literature. The spatiotemporal approach reveals more about locational changes in the city than other methods, which focus only on one component, such as space or time. The study highlights the importance of access to real estate market data, such as data about the short-term rental market, to extract spatiotemporal changes occurring in neighbourhoods. The results contribute to the discourse on the impacts of short-term rental and Airbnb on cities and can be used by policymakers, observers, and citizens to motivate the distribution of services. The question as to whether gentrification or Airbnb came first remains an open one.

Novel Exploratory Spatiotemporal Analysis to Identify Sociospatial Patterns at Small Areas Using Property Transaction Data in Dublin

The residential real estate market is very important because most people’s wealth is in this sector, and it is an indicator of the economy. Real estate market data in general and market transaction data, in particular, are inherently spatiotemporal as each transaction has a location and time. Therefore, exploratory spatiotemporal methods can extract unique locational and temporal insight from property transaction data, but this type of data are usually unavailable or not sufficiently geocoded to implement spatiotemporal methods. In this article, exploratory spatiotemporal methods, including a space-time cube, were used to analyze the residential real estate market at small area scale in the Dublin Metropolitan Area over the last decade. The spatial patterns show that some neighborhoods are experiencing change, including gentrification and recent development. The extracted spatiotemporal patterns from the data show different urban areas have had varying responses during national and global crises such as the economic crisis in 2008–2011, the Brexit decision in 2016, and the COVID-19 pandemic. The study also suggests that Dublin is experiencing intraurban displacement of residential property transactions to the west of Dublin city, and we are predicting increasing spatial inequality and segregation in the future. The findings of this innovative and exploratory data-driven approach are supported by other work in the field regarding Dublin and other international cities. The article shows that the space-time cube can be used as complementary evidence for different fields of urban studies, urban planning, urban economics, real estate valuations, intraurban analytics, and monitoring sociospatial changes at small areas, and to understand residential property transactions in cities. Moreover, the exploratory spatiotemporal analyses of data have a high potential to highlight spatial structures of the city and relevant underlying processes. The value and necessity of open access to geocoded spatiotemporal property transaction data in social research are also highlighted.

Real Estate Market Data Analysis and Prediction Based on Minor Advertisements Data and Locations’ Geo-codes

Real Estate Market Data Analysis and Prediction Based on Minor Advertisements Data and Locations’ Geo-codes

Reverse mortgages through artificial intelligence: new opportunities for the actuaries

In its basic structure, the reverse mortgage (RM) is a contract where a home owner borrows a part or the totality of the future liquidation value of his home at the time of his death. The risks that are borne by the lender are linked to the volatility of the real estate market, that is the house price risk, the financial market risk, that is the interest rate risk, and the uncertainty of the borrower’s lifetime, that is the longevity risk. The quantification of the future liquidation value and its valuation at the issue time is fundamental in the construction of the RM contract either in the perspective of the lender or in the one of the borrower. In the paper, we explore the use of neural networks to project the real estate market data; this approach allows to obtain a predictive analysis of the pricing process and indeed provides a dynamic pricing algorithm.

Spatial dependence model with feature difference

AbstractThis paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro‐level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out‐of‐sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.

Tone in REIT financial statements and institutional investments

ABSTRACTWe investigate the response of institutional REIT investors to the abnormally (net) positive tone in REIT financial statements. For non-REIT firms, sophisticated investors have been found to respond negatively to an abnormally positive tone due to managerial incentives to take advantage of information asymmetries and use a positive tone to manipulate investor perception. However, institutional REIT investors have an informational advantage as they either directly invest in commercial real estate as part of their portfolio management strategy or, at a minimum, have access to commercial real estate market data. Thus, they are able to evaluate the abnormally positive tone in REIT financial statements against their perception of conditions in the commercial real estate and derivative REIT market. For a sample of US REITs over the period of 2001 to 2017, we find that the response of institutional investors to the abnormally positive tone in REIT financial statements is time-varying and non-linear, irrespective of whether we use variables in levels or changes. In particular, in periods of institutional REIT investor optimism (pessimism), institutional REIT investors respond positively (negatively) to an abnormally positive tone and behave as net buyers (net sellers).

RETRACTED ARTICLE: Design of early warning multimedia computer system for real estate market under PROBIT model

The economic benefits brought by real estate are undoubtedly huge for the country and society, but once the real estate market has a serious credit crisis, the whole macroeconomic turmoil will even lead to economic collapse. Many scholars have conducted relevant research on the risk warning of the real estate market, but most of the research is too limited and the early warning system lacks reliable basis and accuracy. Therefore, based on the importance of the real estate economy, it is very important and meaningful to study a system that can accurately and reliably predict the real estate market. This paper abandons the disadvantages of traditional models such as unreliability and innovation based on the Probit model principle. Based on the data of the Nanjing regional real estate market from 2008 to 2018, the macroeconomic variables of this decade and their related real estate economic variables are horizontally and vertically. The real estate market early warning system was constructed and its risk verification analysis was carried out. Finally, based on this system, the real estate market risk of Wuxi City in the first two quarters of 2016 was verified. The verification results show that the early warning rate based on the early warning system proposed in this paper is 85%.

Fundamental Determinants of Real Estate Prices: A Panel Study of German Regions

This paper aims at establishing empirical facts on the fundamental determinants of real estate prices. It contributes to the literature by analysing a unique panel dataset covering a wide range of real estate market data and other economic variables for nearly 100 German cities. Several robust fundamental determinants are identified, among them the supply-side factors of construction activity and housing stock as well as the demand-side factors of apartment rents, market size, age structure, local infrastructure and rental prices. Results suggest that these factors are robustly linked to fundamental real estate prices and thus can be used to detect misalignments of market prices.

The Leading Indicators of the Economic Cycles in Lithuania

Identification of leading indicators that precede economic events and predict the next phase of business cycle is undoubtedly important issue seeking to protect the country against the recession or other negative economic events. The paper analyses potential leading indicators and identifies the best predictors of the business cycles in Lithuania. Various economic, industrial, financial, real estate market indicators as well as consumer and business expectations are analysed in order to find which of them cause the changes in growth rate of GDP. The analysis is based on Granger causality test and autoregressive distributed lag model. The research shows that considered economic indicators are weak predictors of the growth rate of GDP. Volume index of intermediate goods production is the best predictor in the group of industry data as it holds predictive attributes even three years before the changes in economics. That can be also said about two financial indicators, i.e. short-term interest rate and the value of stock market index. Real estate market data such as residential buildings permits and growth rate in house price index can also warn about the changes in the growth rate of GDP two years before. Nevertheless, consumer and business expectations are the most important for prediction of the changes in the growth rate of GDP.DOI: http://dx.doi.org/10.5755/j01.ee.28.3.16705

Change detection using Landsat images and an analysis of the linkages between the change and property tax values in the Istanbul Province of Turkey

In this study, the Istanbul Province was monitored using Landsat 5 TM, MSS, Landsat 7 ETM+, and Landsat 8 OLI imagery from the years 1986, 2000, 2009, 2011, 2013, and 2015 in order to assess land cover changes in the province. The aim of the study was to classify manmade structures, land, green, and water areas, and to observe the changes in the province using satellite images. After classification, the images were compared in selected years to observe land cover. Moreover, these changes were correlated with the property tax values of Istanbul by years. The findings of the study showed that manmade structure areas increased while vegetation areas decreased due to rapid population growth, urbanization, and industrial and commercial development in Istanbul. These changes also explain the transformation of land from rural and natural areas to residential use, and serve as a tool with which to assess land value increments. Land value capturing is critical for the analysis of the linkages between the changes in land cover, and for assessing land transformation and urban growth. Due to inadequate market data, real estate tax values were used to analyze the linkages between detection changes, land cover, and taxation. In fact, the declared tax values of land owners are generally lower than the actual market values and therefore it is not possible to transfer the value increasing of land in urban areas by using property taxation from the owner to local and central governments. The research results also show that the integration of remote sensing results with real estate market data give us to determine the tax base values of real estate more realistically.

Neural Networks Modelling of Municipal Real Estate Market Rent Rates

Abstract This paper presents the results of research on the application of neural networks modelling of municipal real estate market rent rates. The test procedure was based on selected networks trained on the local real estate market data and transformation of the detected dependencies – through established models – to estimate the potential market rent rates of municipal premises. On this basis, the assessment of the adequacy of the actual market rent rates of municipal properties was made. Empirical research was conducted on the local real estate market of the city of Olsztyn in Poland. In order to describe the phenomenon of market rent rates formation an unidirectional three-layer network and a network of radial base was selected. Analyses showed a relatively low degree of convergence of the actual municipal rent rents with potential market rent rates. This degree was strongly varied depending on the type of business ran on the property and its’ social and economic impact. The applied research methodology and the obtained results can be used in order to rationalize municipal property management, including the activation of rental policy.