record_id,doi,title,year,venue,authors,abstract,citations,oa_pdf
8fb62d09e751f9c9ac6b4867faf181a4fa69834c,10.19136/hitos.a25n73.3637,QUANTITATIVE ANALYSIS OF THE BEHAVIOR OF NATIONAL MEXICAN REAL ESTATE PRICES,2019.0,Hitos de Ciencias Económico Administrativas,Michael Demmler; D. Hernández,"OBJECTIVE: To explain Mexican home prices using fundamental, macroeconomic variables in order to identify possible financial bubble tendencies within the Mexican national housing market during the period 2005 to 2018. 
 
MATERIAL AND METHOD: The present study analyzes quarterly data of Mexican real estate market prices and various fundamental, macroeconomic variables during the period 2005 to 2018. The quantitative research approach of the study is based on descriptive statistics and regression analyses. 
 
RESULTS: The main results of the study are as follows: First, a simple comparison between market prices and fundamental values shows some kind of (preliminary) evidence of bubble tendencies on the Mexican national real estate market. Secondly, a more sophisticated regression analysis concludes that especially the fundamental variables outstanding mortgage volume and unemployment rate can explain real estate market price movements almost perfectly. 
 
CONCLUSIONS: The hypothesis of a financial bubble within the national Mexican real estate market is then rejected, in the considered period 2005 to 2018.",2.0,https://revistas.ujat.mx/index.php/hitos/article/download/3637/2779
aa4dd21ef89562a22c3e9bdb3786f1a024bb4334,10.48550/arxiv.2203.14751,Measuring the Impact of Taxes and Public Services on Property Values: A Double Machine Learning Approach,2022.0,arXiv.org,Isaiah Hull; Anna Grodecka-Messi,"How do property prices respond to changes in local taxes and local public services? Attempts to measure this, starting with Oates (1969), have suffered from a lack of local public service controls. Recent work attempts to overcome such data limitations through the use of quasi-experimental methods. We revisit this fundamental problem, but adopt a different empirical strategy that pairs the double machine learning estimator of Chernozhukov et al. (2018) with a novel dataset of 947 time-varying local characteristic and public service controls for all municipalities in Sweden over the 2010-2016 period. We find that properly controlling for local public service and characteristic controls more than doubles the estimated impact of local income taxes on house prices. We also exploit the unique features of our dataset to demonstrate that tax capitalization is stronger in areas with greater municipal competition, providing support for a core implication of the Tiebout hypothesis. Finally, we measure the impact of public services, education, and crime on house prices and the effect of local taxes on migration.",2.0,http://arxiv.org/pdf/2203.14751
e07cc591c6fea66920e0c868c9d079e9c7f8d24c,10.1109/smartgencon60755.2023.10442517,Revolutionizing House Price Prediction: Harnessing the Power of Data Science and Augmented Reality,2023.0,"2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)",B. Kumar; Manisha Kumari; Pallab Banerjee; Pooja Jha,"Data science and augmented reality (AR) technology convergence in recent years has cleared the door for creative applications in many different fields. To revolutionize the process of predicting property prices, this study investigates the merging of data science approaches with AR interfaces. This research proposes an innovative strategy to empower homebuyers and real estate agents by utilizing machine learning algorithms and real-time data visualization through AR. The study is concerned with gathering and analyzing huge datasets about property characteristics, neighborhood characteristics, and market movements. To determine the important variables influencing house prices, sophisticated data analysis approaches are used. Users can experience real estate in an immersive, engaging way by using these insights, which are neatly integrated into an AR application. Users of the AR interface can view prices, property details, and comparative studies in real-time as forecasted housing data is superimposed over it. Prediction accuracy is ensured by including user preferences and dynamic updates, which improve personalization. Through user feedback loops, the system's efficiency is verifie",2.0,
8ff50f352d6819b08901429254c02555f29bf2ef,10.1155/2021/3798183,"Exploring Residential Heterogeneity through Multiscalar Lens: A Case Study of Hangzhou, China",2021.0,Complex,Qinshi Huang; Wei-Xuan Song; Li-Yan Liu; Chun-Hui Liu; Xinyi Zhang; G. He,"The pattern, process, and mechanism of residential heterogeneity vary significantly with different geographical scales. However, most traditional methods ignore the checkboard and modifiable areal unit problem (MAUP), which may cover up the complexity and hierarchy of social space. Taking Hangzhou city as an example, a multiscalar method was proposed based on the information entropy theory to estimate residential heterogeneity and its scale sensitivity. Based on the sixth population census of Hangzhou and the housing price database of 6,536 residential districts from 2008 to 2018, we explore the scale effect and dynamic characteristics of residential heterogeneity. The results of spatial simulation and geostatistical analysis based on Python Spatial Analysis Library (PySAL) module show that the multiscalar algorithm better presents the real segregation pattern than traditional method, which is one of the new models and technologies in urban geography complex system. Exploring residential heterogeneity through multiscalar lens provides an important basis for the gradual and refined urban renewal.",2.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/2021/3798183
ff01a8bc8901c0b7c66958a8dc022e56cb5bf2af,10.1007/s12061-023-09544-9,School-Housing Nexus in Urban China: A Comparative Study of the Effect of School Districts on Commercial and Danwei Housing Prices,2023.0,Applied spatial analysis and policy,Si-Qin Wang; Yuxiao Li; Zhe Gao,"It has been commonly recognised that school quality has significant impacts on housing prices in Western cities, however, how such school-housing relationships vary across different types of housing and urban space in transition economies is less explored. Our study aims to investigate the relationship between schools and housing in transitional China, where both commercial and Danwei housing coexist within the housing sector. This study makes three contributions to the literature. First, this study distinguishes the school-housing relationship across two types of housing—commercial versus Danwei housing as the unique housing characteristics in transition economies. Second, this study fully considers the neighbourhood effect on housing prices and unveils the spatial heterogeneity of school-housing relationships by spatiotemporal analyses and spatial modelling techniques. Third, this study establishes an analytical framework that can be applied to different contexts, and augmented in an assessing manner for the long-term evaluation of future education and housing policies.",2.0,https://eprints.gla.ac.uk/331715/2/331715.pdf
b2591b867cf257561c201008e1bd70c5bb69b40b,10.1007/978-3-030-61757-8_7,Coworking: A Rhetorical Enterprise Situated in Place,2021.0,Good Data in Business and Professional Discourse Research and Teaching,D. Andrews,"Mobile information and communication technology (ICT) and ubiquitous connectivity, among other forces, have led to changes in the concept of knowledge work and “enable[d] a reinvention of where, how and when work happens” (Laing and Bacevice 2013, p. 39). These changes have inspired entrepreneurs to create a new kind of commercial workplace, called coworking spaces, part office with advanced technology and part social setting, available for short- or longer-term rental. To appeal to potential customers, founder-entrepreneurs of such spaces have created, in text and images, virtual representations of their enterprises on their websites. What counts as data in this study are the websites of five coworking spaces. A mixed-methods approach incorporating material culture studies, design thinking, and rhetoric was used to analyze the persuasive structure of these virtual representations. More than mere real-estate, coworking has become a values-driven culture. In an aesthetically pleasing environment, that culture fosters community, mutual support among members of the space, and a bias toward innovation and creativity. This sample, while admittedly small, represents different regions int",2.0,
24715798b163e9c81164c2e641b6a800589cea1d,10.3846/ijspm.2022.16786,FORECASTING SPATIALLY CORRELATED TARGETS: SIMULTANEOUS PREDICTION OF HOUSING MARKET ACTIVITY ACROSS MULTIPLE AREAS,2022.0,International Journal of Strategic Property Management,Changro Lee,"This study involved the development of an approach to forecast house prices and trading volumes across multiple areas simultaneously. Spatially correlated targets, such as house prices, can be predicted more accurately by leveraging the correlations across adjacent areas. A multi-output recurrent neural network, a deep learning algorithm specifically developed to analyze sequence data, was utilized to forecast the house prices and trading volumes in the four chosen study areas. The forecasting accuracy of future house prices in one of the four geographical areas clearly improved; this area was found to be a price-lagging area, and the forecasting accuracy of this area significantly increased by exploiting the information of a price-leading area. As for the prediction of trading volumes, the difference in performance between the multi-output recurrent neural network and conventional models was very small. The results of this study are expected to promote the use of deep learning to predict the housing market activity through a simultaneous forecasting framework.",2.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/16786/11135
5feda1b8a6b6f61a53c959195c057d9f42d7e49a,10.1109/csde59766.2023.10487733,Time Series Forecasting of U.S. Housing Price Index Using Machine and Deep Learning Techniques,2023.0,2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE),K. Nunna; Zhaoxian Zhou; S. Shakya,"The Housing price index (HPI) plays a key role in real estate dynamics, which majorly contributes to the United Sstates economy. Forecasting accurate housing price indices will be very beneficial for investors, policymakers, banks, and homeowners. There was a wide range of approaches implemented for forecasting HPI using microeconomic features with very little concentration on macroeconomic features such as economic indicators at a national level. HPI directly correlates with these macroeconomic features, which have a direct influence on overall real estate dynamics and the economy. In this paper, we applied various time series, machine learning [ML], and deep learning [DL] models to forecast HPI using macroeconomic features. Our goal is to get accurate predictions and get the relationships that directly impact the housing market. Macroeconomic features of the dataset were collected from economic and federal agencies with the aim of forecasting HPI values. We implemented the autoregressive model integrated moving average model [ARIMA] and Facebook Prophet [FB Prophet] to know the temporal dependencies. Next, we applied machine learning and deep learning models Random Forest, Extrem",2.0,
f00153e1a11aff751cce0ac1d8d584adfc2f3843,10.2139/ssrn.3409003,Does Bike Sharing Increase House Prices? Evidence From Micro-Level Data in Shanghai,2019.0,Social Science Research Network,Zheng-Yi Zhou; Hong-Chang Li; An-Ming Zhang,"This paper studies the impact of bike sharing on house prices. We combine the order-level records of a major sharing bike company with the house-level listing data and the Baidu Map point-of-interest (POI) data. We find that bike sharing has a negative effect on house prices. For an average house, the net house price premium caused by bike sharing is -0.48% in the post-launch period. However, the interaction of bike sharing with subway stations has an offsetting positive effect, indicating that bike sharing is a good solution to the “last-mile” problem. Unlike subway stations, the interaction between bus stations and bike sharing does not lead to a premium. At an aggregate level, the net effect of bike sharing is positive (negative, respectively) in zones that are close to (far from, respectively) the city center. This is consistent with our micro-level findings, because the density of subway network decreases with the distance from the city center, and that bike-sharing is more likely to be a complement to the subway network in downtown than in the suburbs.",2.0,https://doi.org/10.2139/ssrn.3409003
b2ca5b926880112da91799e693ad70169f0a4e00,10.1109/icaaic60222.2024.10575745,An Effective Machine Learning Algorithm for Forecasting the Market Value of a House,2024.0,2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC),S. G. Ancy; J. Praveenchandar,"Machine learning plays a virtual role in everyday speech commands, product recommendation, and even medical fields. But instead of providing better customer service, it provides safer autonomous vehicle systems. House price prediction using machine learning is a hot topic in data science and AI. With the increasing availability of data and the advancement of machine learning algorithms, accurately predicting house prices has never been easier. This study will not only assist housing developers and researchers to determine the most significant factors affecting housing prices but will also help determine the best machine learning model to use in this research. This model will allow people to allocate resources to a request without relying on a broker. According to the results of this study, the random forest regression is the most accurate. Real estate market analysis is one of the most important tools for predicting changes in house prices and rental income. Several techniques have been developed in recent years to solve this problem, including statistical models and data mining methodologies and machine learning algorithms. However, a few issues, including data quality and availab",2.0,
5871f97eef8b7d56cc66e1649ca3102bc494e9e5,10.1177/0731121418800271,Correctional Facility Establishments and Neighborhood Housing Characteristics,2018.0,Sociological perspectives,Kelly Mcgeever,"In the 1990s, the United States experienced unprecedented correctional population growth and accommodated the increase by building new facilities. Worries about the negative effect of such facilities on property values were a primary concern and complicated the siting of facilities. The aim of this study was to examine whether establishing correctional group quarters in urban neighborhoods changed housing characteristics. Propensity score matching was used to estimate the effect of establishing a correctional group quarter between 1990–2000 on 2000 median property values, median rent, home ownership rates, and vacancy rates for 12,790 neighborhoods in 124 large U.S. cities. Housing outcomes in neighborhoods with correctional facility sitings did not differ from what would be expected if such establishments were not created. This finding held regardless of the correctional facility type. The concern of declining property values as a result of the introduction of correctional group quarters populations in neighborhoods is generally unwarranted.",2.0,https://journals.sagepub.com/doi/pdf/10.1177/0731121418800271
7137e61015db51053262ac181b480d4924ce46bf,10.1109/icaaic60222.2024.10575519,Data-Driven House Price Prediction,2024.0,2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC),N. Shalini; A. S. Kiran; B. Sathwik; G. T. Reddy; K. Aswini; M. Yadav,"This research study analyzes the complex and dynamic field of real estate, aiming to reveal the numerous factors influencing home values. The primary objective of this research study is to develop a prediction system using Random Forest (RF). Leveraging a comprehensive dataset encompassing historical real estate data, this study intends to forecast house values by considering a wide array of variables. Key attributes such as the number of bedrooms and bathrooms, property size, location, and proximity to amenities like transportation, hospitals, and schools form the cornerstone of the proposed model. Drawing insights from past price fluctuations, the proposed algorithm enhances forecast accuracy by analyzing previous value trends. Additionally, for comparative analysis, the proposed approach incorporates multiple regression and linear regression models alongside the Random Forests approach, enabling to assess the performance of our primary model against industry-standard techniques. The resultant system offers users detailed insights into the local real estate landscape alongside precise property price projections. Users can compare specific properties with nearby alternatives, empo",2.0,
50d38cab08dd36b61065a87cca74d3a25cf15e7d,10.3390/pr10061157,Shallow Fully Connected Neural Network Training by Forcing Linearization into Valid Region and Balancing Training Rates,2022.0,Processes,J. Heo; Chang Gyu Im; K. Ryu; S. Sung; C. Yoo; Dae Ryook Yang,"A new supervisory training rule for a shallow fully connected neural network (SFCNN) is proposed in this present study. The proposed training rule is developed based on local linearization and analytical optimal solutions for linearized SFCNN. The cause of nonlinearity in neural network training is analyzed, and it is removed by local linearization. The optimal solution for the linearized SFCNN, which minimizes the cost function for the training, is analytically derived. Additionally, the training efficiency and model accuracy of the trained SFCNN are improved by keeping estimates within a valid range of the linearization. The superiority of the proposed approach is demonstrated by applying the proposed training rule to the modeling of a typical nonlinear pH process, Boston housing prices dataset, and automobile mileage per gallon dataset. The proposed training rule shows the smallest modeling error and the smallest iteration number required for convergence compared with several previous approaches from the literature for the case study.",2.0,https://www.mdpi.com/2227-9717/10/6/1157/pdf?version=1654764605
84ce9a41dc472e601cd5b9950e6ff81bf61668fb,10.46593/ijaera.2021.v07i05.001,House Price Prediction using Machine Learning in Python,2021.0,International journal of advanced engineering research and applications,N. Kalra; Nidhika Uppal; Prerna Pathak; Muskan Nandkani; Garima Sharma,"The business of buying and selling of house continues to grow every year due to population growth and migration to other cities for their financial purposes. Real estate is a very emerging field in everyone’s day to day life. The prices of houses are regularly changing on daily basis and are sometimes fired rather than based on actual estimates. Foreseeing property costs by actual components is a main criterion of this research paper. Our basic aim is to take all the actual and primary features to determine the result of our system. We have used regression models like decision tree classifier, random forest, and multiple linear regression classifier for prediction to get better results and for upgraded accuracy. This paper will give information that how we will predict the home price with the help of different features and python with its libraries. The main objective of this research paper is the estimation of the market worth of a land, house, property which will help customers to buy and sell property without moving to a specialist.",2.0,https://doi.org/10.46593/ijaera.2021.v07i05.001
d7aa8d4c74b2d15722ca2a4fd5e36016b9d65b09,10.1109/icosec54921.2022.9952023,Location Intelligence Based Smart E-Commerce Platform for Residential Real-Estate Industry,2022.0,2022 3rd International Conference on Smart Electronics and Communication (ICOSEC),Tharindu Balasooriya; Lakindu Kavishka; Isini Dananjana; Maryam Yumna; S. Thelijjagoda; B. Attanayaka; Lanil Marasinghe,"Making the decision to purchase or invest in real estate can be a very crucial process due to its high financial risk. The purchasing decision of residential real estate properties can be even more decisive because, apart from the financial risk, the choice of a property can have a great impact on the future lifestyle of the buyer. When considering residential real estate, one major factor to be considered is the property location. This research sought to determine the applicability of modern technologies such as location intelligence and machine learning in the development of an e-commerce system that may assist users in making optimal residential real estate location decisions. Third-party web APIs were used to obtain location data, and as a result of the study, methodologies were defined to convert location data into meaningful insights by using statistical methods like weighted sum and the analytical hierarchy process. The functionalities in the proposed system have been designed, considering the roles of both buyers and sellers in the real estate business. In the proposed system, the location quality index framework provides overall insights on the location, the personalized i",2.0,
a1d448944155043a92099df6473158694642135e,10.3390/buildings14103172,Machine Learning Valuation in Dual Market Dynamics: A Case Study of the Formal and Informal Real Estate Market in Dar es Salaam,2024.0,Buildings,Frank Nyanda; Henry Muyingo; Mats Wilhelmsson,"The housing market in Dar es Salaam, Tanzania, is expanding and with it a need for increased market transparency to guide investors and other stakeholders. The objective of this paper is to evaluate machine learning (ML) methods to appraise real estate in formal and informal housing markets in this nascent market sector. Various advanced ML models are applied with the aim of improving property value estimates in a market with limited access to information. The dataset used included detailed property characteristics and transaction data from both market types. Regression, decision trees, neural networks, and ensemble methods were employed to refine property appraisals across these settings. The findings indicate significant differences between formal and informal market valuations, demonstrating ML’s effectiveness in handling limited data and complex market dynamics. These results emphasise the potential of ML techniques in emerging markets where traditional valuation methods often fail due to the scarcity of transaction data.",2.0,https://www.mdpi.com/2075-5309/14/10/3172/pdf?version=1728119444
31e3e1eed4f48bb50490c953ff77f45a0be50276,10.3390/su16167087,"An Approach to Analyzing Migration Flow and House Price Data: Exploring Spatial Linkages between Flow, Price, Distance, Age at the Local Level",2024.0,Sustainability,T. Le; Le-Vinh-Lam Doan; Hoang Nam Le,"This paper is intended to elucidate the efficacy of employing a Geographic Information Systems (GIS) methodology to investigate the spatial interrelations among migration flows, housing prices, age demographics, and geographical distances. Utilizing data on migration flows and housing prices from Greater Manchester, UK, in 2011, in this study, we performed statistical analyses and generated a suite of visual mappings to illustrate the spatial dynamics between migration patterns, distances, age brackets, and the segmentation of migration destinations by housing price tiers. The findings reveal pronounced spatial associations among migration destinations, underscored by the interplay of housing price levels with age categories, distance intervals, or the origination points tied to specific housing price ranges. This GIS-based analytical framework offers policy-makers and urban planners a powerful tool for discerning distinct migration trends and locales, thereby facilitating more informed decision-making processes.",2.0,https://www.mdpi.com/2071-1050/16/16/7087/pdf?version=1723979622
2ffae320868c1d23098f5c880ffa95ceb3b3d7d2,10.1080/17477778.2024.2375446,Mitigating housing market shocks: an agent-based reinforcement learning approach with implications for real-time decision support,2024.0,J. Simulation,Sedar Olmez; Alison J. Heppenstall; Jiaqi Ge; C. Elsenbroich; Dan Birks,"ABSTRACT Research in modelling housing market dynamics using agent-based models (ABMs) has grown due to the rise of accessible individual-level data. This research involves forecasting house prices, analysing urban regeneration, and the impact of economic shocks. There is a trend towards using machine learning (ML) algorithms to enhance ABM decision-making frameworks. This study investigates exogenous shocks to the UK housing market and integrates reinforcement learning (RL) to adapt housing market dynamics in an ABM. Results show agents can learn real-time trends and make decisions to manage shocks, achieving goals like adjusting the median house price without pre-determined rules. This model is transferable to other housing markets with similar complexities. The RL agent adjusts mortgage interest rates based on market conditions. Importantly, our model shows how a central bank agent learned conservative behaviours in sensitive scenarios, aligning with a 2009 study, demonstrating emergent behavioural patterns.",2.0,https://doi.org/10.1080/17477778.2024.2375446
3939936e88c9a1c978f871703b3aabf3f2f8b666,10.1109/icpct64145.2025.10939118,Integrating Random Forest and XGBoost For Accurate House Rent Prediction,2025.0,2025 International Conference on Pervasive Computational Technologies (ICPCT),H. Singh; S. Srivastava; N. Pandey; Nitesh Kumar,"Predicting house rents accurately is essential for tenants, landlords, and investors to make informed decisions in the real estate market, particularly given the rising trends in rental prices. This study employs machine learning techniques, including Random Forest and XGBoost, to predict rental values based on factors such as location, area, square footage, rent, and house size. The dataset was preprocessed using Python libraries such as NumPy, pandas, and scikit-learn, with steps including data cleaning, normalization, and feature engineering. Features such as house age, floor details, and distance from city centers were derived to enhance model performance. Evaluation metrics like Root Mean Squared Error (RMSE) were used to measure accuracy. The Random Forest algorithm achieved superior results with its robustness against overfitting and its ability to model complex relationships. XGBoost, with its scalability and speed, also delivered competitive performance. The ensemble approach of these models further improved prediction accuracy by leveraging their individual strengths.The developed system achieved exceptional performance metrics, including a Mean Squared Error (MSE) of 0.0",2.0,
775dc4f0c0983aa1cbc7ae530f38ce125a1256cb,10.1080/07350015.2018.1513845,Double-Question Survey Measures for the Analysis of Financial Bubbles and Crashes,2018.0,Journal of business & economic statistics,M. Pesaran; I. Johnsson,"ABSTRACT This article proposes a new double-question survey whereby an individual is presented with two sets of questions; one on beliefs about current asset values and another on price expectations. A theoretical asset pricing model with heterogeneous agents is advanced and the existence of a negative relationship between price expectations and asset valuations is established, and is then tested using survey results on equity, gold, and house prices. Leading indicators of bubbles and crashes are proposed and their potential value is illustrated in the context of a dynamic panel regression of realized house price changes across key Metropolitan Statistical Areas (MSAs) in the U.S. In an out-of-sample forecasting exercise, it is also shown that forecasts of house price changes (pooled across MSAs) that make use of bubble and crash indicators perform significantly better than a benchmark model that only uses lagged and expected house price changes. Supplementary materials for this article are available online.",2.0,https://figshare.com/articles/dataset/Double-Question_Survey_Measures_for_the_Analysis_of_Financial_Bubbles_and_Crashes/7063808
51028e8115867a4c4e6210e22425ee30c4d7bfc6,10.1108/ijhma-02-2024-0020,Testing the relationship between housing prices and inflation in the OECD countries,2024.0,International Journal of Housing Markets and Analysis,A. Rufai; R. L. Aor; A. Salisu,"
Purpose
This study aims to construct alternative models to establish the dynamic nexus between inflation and housing prices by estimating the short- and long-run relationship between housing prices and inflation for 15 OECD countries from 1980Q1 to 2022Q4. Furthermore, the authors examined this association using the core and headline inflation and price-income and price-rent ratios as proxies for inflation and housing prices, respectively.


Design/methodology/approach
The authors use the panel autoregressive distributed lag technique to examine the nexus between housing prices and inflation to capture the distinct characteristics of the sample countries, estimate various short-run and long-run dynamics cum separate analyses for turbulent and calm periods in the relationship between housing prices and inflation.


Findings
Changes in housing prices have a greater impact on core inflation than headline inflation. Overall, the authors establish a positive (negative) relationship between housing prices and core inflation in the long run (short run) based on alternative proxies of housing prices. However, this connection tends to be less significant for headline inflation and episodic",2.0,
ef26ddd2d4618a26277b45bba61d442685fed9cf,10.1109/cises58720.2023.10183626,Real Estate Property Price Estimator Using Machine Learning,2023.0,2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES),Disha Ub; Surabhi Saxena,"Property price estimator is a mobile application by creating a model using machine learning to forecast the sale price of homes in a Bangalore. This model will be built using data on a variety of house features, such as location, size, age, and condition, as well as the sale prices of the houses. In order to commence, we have gathered and prepared the required data so that it is available for analysis. Managing missing or inaccurate numbers and making sure the data is consistent and appropriately reflects the properties and their sale prices may be involved in this. Next, we will perform exploratory data analysis (EDA) to better understand the relationships between the various house features and the sale prices. This involves visualizing the data and identifying patterns and trends that may be useful for building the prediction model. After having a good understanding of the data, next comes building and training the prediction model. This involves selecting and optimizing an appropriate machine learning algorithm, such as linear regression or a decision tree, as well as selecting and engineering relevant features for the model. The model will then be put through its paces using su",2.0,
d7678592e66e90720a5e7fc4a8fc482ba1663f6a,10.3386/w28851,Foreclosure Spillovers within broad Neighborhoods,2018.0,Social Science Research Network,Wei-Ran Huang; A. Nelson; Stephen L. Ross,"This paper tests for the spillover effects of foreclosure within broad neighborhoods. The best evidence that foreclosures have causal, spillover effects on housing prices and future foreclosures suggest highly localized spillover effects that are modest in magnitude, but these effects could multiply when the density of spillovers is high leading to larger aggregate effects in broader neighborhoods. We test this proposition by developing a proxy for the fraction of housing units/mortgages that are expected to be in negative equity during the crises. This proxy exploits the timing of purchases in each tract during the run up to the crisis, and we show that our source of identification, within tract variation in purchases over time, is not predicted the observed mortgage attributes. Our estimates suggest that 67 percent of the increase in the across tract dispersion in the recording of new foreclosure recordings can be explained by the spillover effects of the contemporaneous stock of foreclosures.",2.0,https://doi.org/10.3386/w28851
6b229e0550558f9d8cf71f8ed102b8402d77a4f4,10.2139/ssrn.4532561,Short-term Rentals and Housing Market: Evidence From Portuguese Metropolitan Areas,2023.0,Social Science Research Network,Francisco Nobre; Diogo Jardim Gonçalves; Ronize Cruz,"In this paper, we make use of the rapid expansion of short-term rentals in Portugal, based on a policy change in 2014, to estimate the effects on house prices. Using a novel dataset consisting of property transaction data, from 2010 to 2017, for the metropolitan areas of Lisbon and Porto, we causally identify the impact of these reforms through a two-way fixed effects model, at the quarterly level, where we control for property-specific characteristics and location and time fixed effects. The evidence suggests that a one-unit increment in the number of local lodging establishments results in a 0.17% increase in the value of transaction, which is ensured by a set of robustness exercises. Stronger effects are found for properties with four or more bedrooms, owned by citizens outside of the European Union, in the municipality of Porto and at the upper quantiles. We also document a decrease in the number of transactions of new buildings and a positive effect on the value of commercial properties",2.0,https://doi.org/10.2139/ssrn.4532561
42e67bd03d57a94853543cd5b6faf7d2fc716c1d,10.5220/0013511000004619,AI in Real Estate: Forecasting House Prices with Advanced Machine Learning Models,2024.0,Proceedings of the 2nd International Conference on Data Analysis and Machine Learning,Jia-Shuo Cui; Zhitong Liu; Yinghan Ma,": Homes are essential to our lives, providing a comfortable space for living, working, and learning. Therefore, everyone needs a place to call home. This article describes how to use different machine learning algorithms (one of the core techniques of artificial intelligence, including linear regression, decision trees, random forests, and XGBoost) to complete the task of predicting house price trends at a time when house price trends are becoming the focus of attention. The performance of each model is compared to evaluate its different advantages and disadvantages in predicting housing prices. The results show that XGBoost model is the most effective in predicting house prices and becomes the best choice, followed by random forest model linear regression model, decision tree model can play a role in some cases. The results of the experiment can not only be convenient for home buyers or real estate developers to get help, but also can be used as a reference for government decision-making.",2.0,https://doi.org/10.5220/0013511000004619
daf65dd7c289554c16b459a42025ac91b3e26f79,10.58905/sana.v2i1.265,Analysis of K-NN Algorithm and Linear Regression to Predict House Prices in Jabodetabek,2024.0,"SaNa: Journal of Blockchain, NFTs and Metaverse Technology",Nadia Putri Ariyanti; Agung Triayudi; Ratih Titi Komala Sari,"Jabodetabek is now the region with the highest average level of citizen satisfaction, so many people migrate to this region in the hope of getting better living conditions, this will make people who want to buy a house question whether the house they want to buy is good value or not. The purpose of this study is to evaluate the effectiveness of multiple linear regression and K-Nearest Neighbors (KNN) algorithm on a dataset of house prices in Jabodetabek. Better results are obtained by using the Multiple Linear Regression model which has lower Mean Absolute Error (MAE) and Mean Squared Error (MSE) values and a fairly good R-squared of around 48.72%. However, the very high MAE and MSE values of the KNN model indicate inaccuracy and significant prediction variance. Although KNN has a relatively high R-squared value, more research is needed to see if the model can adequately explain data fluctuations. Based on the performance evaluation, multiple linear regression is ultimately a better choice",2.0,https://journal.mediadigitalpublikasi.com/index.php/sana/article/download/265/118
f713572264ac56c9c4aca13c56904d8f47e836ac,10.20937/rica.2019.35.01.15,EFFECTS OF NOISE EMITTED BY MILITARY AIRCRAFT ON HOUSING CLOSE TO AN AIR BASE: THE CASE OF GANDO AIR BASE (SPAIN),2019.0,Revista Internacional de Contaminación Ambiental,José Francisco Rodríguez-Artiles; A. Pérez-Zabaleta; José María Labeaga-Azcona,"Assessing the impact of environmental externalities has become one of the essential aspects in making economic and urban planning decisions. Our goal focuses on the innovative assessment of noise pollution emitted by military aircraft located at Gando Air Base in 2012. Its impact on the housing market was analyzed for 6447 households affected by more than 55 decibels, limiting our study to three municipalities of Gran Canaria (Spain): Telde, Aguimes and Ingenio. We propose to test several specifications for the hedonic price model and we test among them. This is a basic pre-requisite to estimate the parameters needed to inform policy markets. Our preferred model is a semi-logarithmic specification. We conclude from its results that noise pollution emitted by military aircraft positively affects the price of housings in the area when they are between 55 and 70 decibels so the fact that a property is near a noise source is not in itself conclusive evidence of a loss of real state values.",2.0,https://www.revistascca.unam.mx/rica/index.php/rica/article/download/RICA.2019.35.01.15/46820
d8d5366d79a95892c2cdcad05b8040841c7d531e,10.1080/10511482.2026.2665273,Unequal Residential Gains and Losses Under Rising Housing Prices: Evidence from Longitudinal Relocation Behavior,2026.0,Housing Policy Debate,Ling-Yu Li; Ping Huang; Yusi Luo; Shujin Yu,"Abstract Rising housing prices are a prominent feature of contemporary urban housing markets. While displacement research has largely emphasized the adverse residential outcomes among disadvantaged groups amid price escalation, less is known about the potential gains for other socioeconomic groups. We construct a multidimensional relocation assessment framework to examine how local housing price growth shapes residents’ residential outcomes in Shenzhen, China. We integrate housing transaction records from 3,829 communities with longitudinal mobile signaling data and apply fixed-effects models. The results show that price growth, especially over three- to five-year periods, is associated with lower move rates. When moves do occur, they diverge into two distinct outcomes. Residential gains include shorter moving distances, relocation toward central areas, and moves to higher-quality neighborhoods with improved travel convenience and better access to urban amenities, whereas residential losses manifest as relocating to lower-priced areas. Further analysis shows clear heterogeneity: gains are concentrated among formal housing residents, older cohorts, and those located in central areas",2.0,
43a0a43d6cb55702dd93d64e6e7848510866292e,10.2139/ssrn.3774556,Identifying consumption profiles and implicit cross-subsidies under fixed-rate electricity tariffs,2021.0,Social Science Research Network,Gordon W. Leslie; Armin Pourkhanali; G. Roger,"Wholesale electricity prices can rapidly change in real-time, yet households usually face fixed-price electricity tariffs. Therefore, households that predominantly use energy when wholesale prices are low implicitly cross-subsidize households whose energy use is more weighted to high-price periods. We develop a decomposition method that maps substation data on electricity use to demographic data, identifying the household characteristics associated with this cross-subsidization in Victoria, Australia. We find that households in areas with low house prices and high levels of renters and elderly residents are the net funders of this implicit subsidy. These households currently have the lowest average energy cost for retailers to service, and may be the greatest immediate beneficiaries if real-time retail tariffs are made available to them. Finally, we present evidence that cross-subsidy magnitudes have been growing in recent years, coincident with rapid solar generator penetration.",2.0,https://doi.org/10.2139/ssrn.3774556
727e65cb483ccea23927180957db6e0c4e7f3a29,10.1109/i-smac61858.2024.10714734,Enhancing Housing Price Prediction Using AI and Machine Learning: A Stacked Regression Meta-Modeling Approach,2024.0,"2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)",Malgi Nikitha Vivekananda; Prashant Ashok Shidlyali,"Predicting housing prices is crucial in real estate markets, enabling informed decision-making for investors, buyers, and policymakers. Many studies have applied various machine learning models to this task, either focusing on individual models or overlooking the benefits of combining multiple models through ensemble techniques. Additionally, the neglect of optimizing model performance through feature selection and hyperparameter tuning often results in suboptimal outcomes. To address these gaps, we propose a meta-modeling and optimization workflow that integrates multiple regression models—such as Random Forest, Ridge, Lasso, and Support Vector Regression (SVR)—using a stacked regressor to improve prediction accuracy. Our workflow includes systematic hyperparameter tuning and feature selection to refine model performance. The stacked model outperforms individual models, achieving an R² score of 0.9321 and an RMSE of 1.5017, representing a 6.5% improvement in R² and a 39.2% reduction in RMSE compared to the best individual model, Random Forest. This demonstrates the effectiveness of ensemble methods in capturing complex relationships in housing data. Furthermore, our proposed workf",2.0,
f985188013feb2551466014b75b5e8b42de0e357,10.55041/ijsrem37950,Predicting House Prices Using Regression Model,2024.0,INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,Rajiv Tulsyan; Pranjal Shukla; Tushar Singh; Anshul Bhardwaj,"This paper shows a study on the development of a predictive model for house prices. The model uses machine learning techniques to analyze a large dataset of of housing market data, including demographic information, real estate trends, and economic indicators. The goal of the model is to accurately predict the sale prices of a house given its features and traits. To achieve this target, the study employs feature selection and engineering methods to determine the most significant predictors of house prices [1]. The results of the model are evaluated using standard metrics, sucha as root mean squared error [RMSE] and mean absolute error [MAE]. The results show that the proposed model outperforms benchmark model and provides a reliable prediction of house prices. The model can be used by real estate professionals, policymakers, and homebuyers to gain insights into the housing market and make the right choice [3]. Keywords- Regression, prediction, house price, Machine Learning.",2.0,https://doi.org/10.55041/ijsrem37950
3af0709f3752ca1c3b6903224f3c99341b3e7a54,10.1111/lsq.12458,"Legislating landlords: Private interests, issue emphasis, and policy positions",2024.0,Legislative Studies Quarterly,Stefan Müller; Jihed Ncib,"Do private interests predict politicians' rhetoric? Focusing on housing policy, we compare issue emphasis and positions of landlord politicians and politicians who do not own multiple properties. Ireland provides a unique opportunity to study legislating landlords' behavior as housing has become one of the most important political issues. We construct a novel dataset of politicians' homeownership status between 2013 and 2022, a period characterized by rising rent and property prices. We fine‐tune a transformer‐based machine learning model and apply text scaling and sentiment analysis to identify issue salience and positions on housing in over 870,000 tweets and parliamentary questions. Contrary to our expectations, landlord politicians do not avoid the topic of housing, nor do they take different positions. We also find that government status does not influence this relationship. The results imply that private financial interests do not influence rhetoric on housing policy.",2.0,https://doi.org/10.1111/lsq.12458
6e51001070b404dffff70ec1499b1e3a48e2593f,10.1108/ijhma-03-2025-0063,Sustainable development and housing market trends: the influence of CO2 emissions and macroeconomic factors,2025.0,International Journal of Housing Markets and Analysis,Sunil Tiwari; Kok-Shi-Au Hui; Arshian Sharif; C. W. H. Cheong,"Purpose
This study aims to investigate the relationship between housing price (HP) and the macroeconomic determinants such as gross domestic product (GDP), CO2, inflation (INF) and unemployment (UNEMP).

Design/methodology/approach
To examine the dynamic functioning of HPs due to these indicators, the study uses the quantile autoregressive distributed lagged (QARDL) technique to extract the detailed influence in quantile form. However, the Wald test and Granger causality test are implied in the data to verify the long- and short-run associations.

Findings
The results of the QARDL indicated that except for UNEMP, other factors like INF, GDP and CO2 are positively significant with HPs throughout the quantiles, but UNEMP is negatively significant with HP. Moreover, Granger causality shows the bidirectional relationship among the variables.

Originality/value
Worldly advancements and financial crises accelerated at the same pace, due to which instability and uncertain consequences occur globally. One of the noticeable issues is the indeterminate real estate prices; thus, this study highlighted this issue and conducted the study in Malaysia. Considering these results, the last section ",2.0,
a4dad09d957d5f9fd70b7361f4c01d0efbbe2240,10.1080/15309576.2021.1955218,All Is Not LOST in Texas? An Outcome-Oriented Analysis of Local Option Sales Tax Choices on Measures of Economic Growth,2021.0,Public Performance & Management Review,David Z. Kanaan; Jeremy L. Hall; Huan Qin,"Abstract Are municipal-level local option sales taxes (LOST) in Texas associated with long-term economic development outcomes? The Texas 4a and 4b LOST options fund economic development projects managed by a dedicated municipal-level economic development corporation (EDC). Instead of short-term outputs, we consider associations with three primary long-range outcomes: property value growth, population growth, and new home construction. Three regression analyses examine economic growth between 2011 and 2015 for Texas municipalities while controlling for county-level nesting effect, local capacity, and other contextual factors. The implementation of the 4a industrial development LOST shows a small significant positive association with population growth, albeit at the .10 level. The 4a LOST is also selected as part of the optimal model for property value growth despite not being significant. Implementation of the broader 4b community development LOST is not found significant nor included in any of the three outcome-based models. In each case, the county-level nesting effect is highly associated with economic development outcomes, while municipal-level capacity measures, such as propert",2.0,
eec415b8a5e8a2e9572ed6ad0967293c63d9e6a4,10.4337/9781788119962.00042,Commoning property in the city: the ongoing work of making and remaking,2020.0,,Anna K. Kruzynski,"Gentrification is a global force affecting urban communities across the world (Simet, 2016). Point St. Charles, an urban area located in close proximity to downtown Montreal in Quebec, Canada, is no exception. Originally hunting and fishing grounds of the Kanien’kehá:ka and other Indigenous peoples, the areas’ marshlands were enclosed by colonizers for agricultural and residential purposes (Greer, 2012). Later redeveloped for industrial purposes, this “urban village” of 13,000 souls, has been the terrain of struggle against gentrification for the past three decades. In the wake of policy adopted by Montreal City officials and supported by capitalist real estate developers, the vacant factories along the Lachine Canal were converted into luxury condominiums. More recently, with the expansion of the downtown, “affordable” condominium developments have multiplied, as has the conversion of multiplex buildings into single-family homes by middle-class professionals wanting to settle close to Montreal’s urban center. Property values have skyrocketed as properties are bought up to be renovated and “flipped” onto the capitalist market for profit. In just a decade, average rent has increased",2.0,
ac615d37078ef3b012caea434ec7d0c9d60320cf,10.4028/www.scientific.net/amr.1079-1080.1194,Research on the Long-Term Memory of Commodity Housing Price Volatility Based on the FIGARCH Model,2014.0,,F. Lan; Ba-Hua-Chao Chen,"The purpose of this paper is to test whether there exists a long-term memory volatility characteristics of housing price. The paper based on the data ranging of Zhengzhou from January 2004 to May 2014, by adopting the FIGARCH model, empirically studies and analysis this characteristics. The research results indicate that the price fluctuation of Zhengzhou commodity homes exist effect of cluster and long-term memory characteristic. FIGARCH model can capture the long memory well, and can predict the future price of commodity residential house for a period of time .Therefore, FIGARCH model can well catch long-term memory and forecast the commodity housing price in the future period of time, which illustrates that external shocks have long-standing impact on the volatility of commodity housing price as well, reaching the conclusion that long-effect Mechanism of regulation and control should be set and developed during the macro-control of the government.",2.0,https://doi.org/10.4028/www.scientific.net/amr.1079-1080.1194
81093333190e97f28a210bdda34784ca3820c416,10.1108/ijsms-07-2025-0345,Economic impact of the Winter Olympics: examination of temporary housing prices,2025.0,International Journal of Sports Marketing & Sponsorship,Chan Hyeon Hur; Nicholas M. Watanabe; B. Soebbing; Grace Yan,"
 
 This study examines the net impact of the 2018 Winter Olympics on temporary housing prices in Gangneung, South Korea, from 2012 to 2023. Amid growing scrutiny of the Games' long-term economic sustainability, particularly in smaller, less urbanized regions, this study offers empirical evidence on localized housing market effects in a non-Western context and assesses the broader, often untested, economic and social benefits of the Olympics.
 
 
 
 Employing the synthetic control method, this study compares rental prices in Gangneung with rental prices in non-host cities nationwide exhibiting similar economic and housing characteristics.
 
 
 
 Contrary to expectations of economic gains from hosting the Games, the findings indicate no significant increase in monthly rent prices in Gangneung relative to other cities. In fact, rental prices showed limited change during the Olympics and in the immediate years following, challenging the notion that hosting such mega-events generates positive fiscal returns.
 
 
 
 The findings offer important insights into the discourse on the intangible benefits of winter sport mega-events and provide lessons for planning sustainable future initiativ",2.0,
f3055fd53617327c75cb56b5dc3888ac15e18f21,10.1111/sjtg.12560,Suburban dynamics: A study of migration and governance in suburban Kolkata,2024.0,Singapore journal of tropical geography,Riya Bhattacharya; Debarshi Guin,"Drawing from an analysis of the processes of migration and governance, this paper documents the suburban dynamics of Dankuni—an industrial town located on the western periphery of Kolkata. While in the last few decades, a significant portion of suburban Kolkata has witnessed economic and demographic stagnation or decline, Dankuni has grown by attracting migrants mostly from nearby suburban towns/cities rather than from the city core or distant rural areas. The town also has a robust industrial foundation which provides gainful employment to the majority of its working residents underscoring their independence from the central city for livelihoods. Although the town's growth is driven by the sustained flow of migrants, they are unevenly distributed as majority of them prefer to settle in the inner part of the town where civic infrastructure and communication facilities are better than those in other parts of town. However, over time, especially since the establishment of the municipality in 2008, property prices swiftly escalated in this most sought‐after zone. Consequently, lower‐income migrant households have filtered out to the outer parts of the town. This spatial filtering has ",2.0,
c1bfd771c6afbcf3b81f42a914679e6b972e73e6,10.3390/math12030390,Ridge-Type Pretest and Shrinkage Estimation Strategies in Spatial Error Models with an Application to a Real Data Example,2024.0,Mathematics,Marwan Al-Momani; M. Arashi,"Spatial regression models are widely available across several disciplines, such as functional magnetic resonance imaging analysis, econometrics, and house price analysis. In nature, sparsity occurs when a limited number of factors strongly impact overall variation. Sparse covariance structures are common in spatial regression models. The spatial error model is a significant spatial regression model that focuses on the geographical dependence present in the error terms rather than the response variable. This study proposes an effective approach using the pretest and shrinkage ridge estimators for estimating the vector of regression coefficients in the spatial error mode, considering insignificant coefficients and multicollinearity among regressors. The study compares the performance of the proposed estimators with the maximum likelihood estimator and assesses their efficacy using real-world data and bootstrapping techniques for comparison purposes.",2.0,https://www.mdpi.com/2227-7390/12/3/390/pdf?version=1706189682
e0194a8ec6f97cc08f6d03e43afd05b37442fea0,10.37043/jura.2022.14.1.1,INVERSE MODEL USING LAND AND PROPERTY SUB-SYSTEMS FOR PLANNING FUTURE CITIES: A GENERAL FRAMEWORK,2022.0,Journal of Urban and Regional Analysis,R. Crespo; A. Rajabifard,"This paper suggests, based on literature review, the use of the inverse model coupled with land and property systems to support urban decision-making. The inverse model is to be used for planning decisions today to achieve the desired tomorrow. This approach has been used previously in urban planning with a property system. The use of a property system alone is insufficient in dealing with the complexity of urban systems. Complex systems are made up of sub-systems that interact with each other; the integration of two sub-systems offers a first and simple alternative to address the complexity of urban systems. We suggest the use of two parametric approaches, logistic regression and house price, to model land and property sub-systems, respectively. Finally, we stress that further studies are needed to integrate the inverse model with other statistical techniques that also deal with complexity, such as cellular automata (CA) or agent-based models.",2.0,https://www.jurareview.ro/resources/pdf/volume_33_inverse_model_using_land_and_property_sub-systems_for_planning_future_cities:_a_general_framework_abstract.pdf
2e069ae24efe99a514d1a29e37e21a53ba48cdde,10.1108/jfep-02-2018-0025,A comparative study between the UK and the USA house price indicators before and during the financial crisis of 2007-2009,2018.0,Journal of Financial Economic Policy,Abdelhafid Benamraoui,"
Purpose
This paper aims to examine the relationship between key economic fundamentals and average house price (AHP) movements before and during the financial crisis of 2007 to 2009 in the UK and the USA.


Design/methodology/approach
Multiple regression analysis is applied in assessing the correlation between AHPs and a set of selected economic fundamentals.


Findings
The study results show that earnings and to less extent interest rate have the highest correlation with the AHP and among the different types of interest rate used variable interest rate has the strongest correlation with AHP. The results also reveal that most indicators behave in the same way both before and during the financial crisis, but with better explanatory power for the pre-crisis period. Another key finding is that the directions of relationship for some of the parameters have changed when the market is in crisis, especially in the case of loans extended to house purchase for the UK market and number of households for the US market.


Originality/value
The originality of the paper stems in using a wide range and thoroughly selected economic fundamentals to explain the movement in house prices and to observ",2.0,
db45b6585af6d91680e9dc8d223253bea52b9702,10.33564/ijeast.2021.v05i11.041,ANALYSIS OF DIFFERENT REGRESSION MODELS FOR REAL ESTATE PRICE PREDICTION,2021.0,International Journal of Engineering Applied Sciences and Technology,Pranav Kangane; Aadesh Mallya; A. Gawane; V. Joshi; Shivam Gulve,"The housing market is a standout amongst the
most engaged with respect to estimating the price and
continues to vary. Individuals are cautious when they are
endeavoring to purchase another house with their financial
plan and market strategies. Consequently, making the
housing market one of the incredible fields to apply the
ideas of machine learning on how to enhance and
anticipate the house prices with precision. The objective of
the paper is the prediction of the market value of a real
estate property and present a performance comparison
between various regression models applied. Nine
algorithms were selected to predict the dependent variable
in our dataset and then their performance was compared
using R2 score, mean absolute error, mean squared error
and root mean squared error. Moreover, this study
attempts to analyze the correlation between variables to
determine the most important factors that are bound to
affect the prices of house.
",2.0,https://doi.org/10.33564/ijeast.2021.v05i11.041
0f0224520307b2dd2be4e6bbbb275f0944da01db,10.1080/02692171.2018.1518412,"Natives, immigrants and social cohesion: intra-city analysis combining the hedonic approach and a framed field experiment",2018.0,International Review of Applied Economics,R. Borgoni; Giacomo Degli Antoni; Marco Faillo; A. Michelangeli,"ABSTRACT The literature on the hedonic price approach applied to housing highlights the existence of natives’ preferences against living in urban areas with high foreign-born population. At the same time, empirical and experimental evidence show that ethnic fragmentation reduces social cohesion in society. Mainly because of the difficulty to measure social cohesion at the neighborhood level, the correlation between these two phenomena is still largely unexplored. In this paper, we investigate natives’ preferences for immigrants following an original approach that combines the hedonic price approach and a framed field experiment. The latter allows us to collect a measure of cooperation at the neighborhood level. We apply this methodology to the city of Milan. Our findings show that natives prefer to not live in dense immigrants neighborhoods. However, this preference is not attributable to an erosion of social cohesion in those areas.",2.0,http://hdl.handle.net/10281/282096
cb989f959c3984fa04267feef406a28d263da44d,10.21307/ijanmc-2020-001,Research on House Price Prediction Based on Multi-Dimensional Data Fusion,2020.0,"International Journal of Advanced Network, Monitoring, and Controls",Yonghui Yang,"Abstract The price of commercial housing is related to the process of urbanization in China and the living standard of residents, so the prediction of the price of commercial housing is very important. A major difficulty in predicting regression problems is how to handle different attribute types and fuse them. This paper proposes a house price prediction model based on multi-dimensional data fusion and a fully connected neural network. The model building steps are: First, normalize the data involved in the sample; then, interpolate the normalized data to increase the data density; subsequently, the normalized sample data is converted into a pixel matrix; finally, a fully connected neural network model is established from the pixel matrix to the price of the commercial house. After the neural network model has been established, the price of house can be obtained by entering the attributes of the house into the neural network model.",2.0,https://doi.org/10.21307/ijanmc-2020-001
0b36f197b797b5d0927f704338eb4f587fa0299b,10.54097/hset.v31i.4828,Transaction price prediction of second-hand houses in Wuhan based on GA-BP model,2023.0,Highlights in Science Engineering and Technology,Can Peng; Heng Xiao; Kai-Man Ou,"The significance of the used housing market to the stability of people's lives and social development is increasing, and the prediction of its prices is becoming a key concern for society. In this paper, BP neural network model is used to predict the price of second-hand properties. In view of the disadvantages of slow convergence and the tendency to obtain local optimal solutions, the BP neural network model's input layer is optimized by a genetic algorithm to speed up the convergence speed and accuracy of the BP neural network model. The experimental results show that the improved GA-BP neural network has high prediction accuracy, with RMSE and MAE of 398.72 and 170.18, respectively. The difference between the actual and predicted values is small, which can provide a more stable tool for predicting house prices in the second-hand property market and broaden a new channel for the practical application of GA-BP neural network.",2.0,https://drpress.org/ojs/index.php/HSET/article/download/4828/4684
c668f47b665583eee00ab23aa66be21c40941906,10.1007/s11146-025-10012-z,The Effect of Increased Police Spending on House Prices and Sales Volume: A Tale of Two Types of Cities,2025.0,Journal of real estate finance and economics,David M. Brasington,"We present the first capitalization study to look at crime fighting and house prices using the causal inference technique of regression discontinuity. It is also the first study on the link between police spending and housing sales volume. Voting to increase police taxes and spending by 15% is not linked to house prices or transaction volume overall. However, increasing spending causes 13% higher housing prices in low-income cities and at least a 14% decrease in house prices in high-income cities. The effects persist through all five years after the vote that we study. The sale price results are most consistent with overfunded police departments, rather than Tiebout sorting, neighborhood instability, or signaling. Our results suggest that the small or non-existent link between house prices and crime found by the literature really just reflects the sum of large but opposite moves in house prices in different market segments.",2.0,https://link.springer.com/content/pdf/10.1007/s11146-025-10012-z.pdf
4e3cfbb9566ac3868ae84688075d57032e06528b,10.1093/oso/9780198838104.003.0009,Spillover Effects of Unconventional Monetary Policy on Asia and the Pacific,2019.0,Macroeconomic Shocks and Unconventional Monetary Policy,M. T. Punzi; P. Chantapacdepong,"The chapter assesses the evolution of spillover effects of unconventional monetary policies (UMPs) and their macroeconomic impact on Asia and the Pacific region. It develops a Panel Vector Auto Regression model for a period covering data from first quarter 2000 until first quarter 2015. It finds that Asia and the Pacific region has responded to the advanced economies’ actions with accommodative monetary policy. Such lower interest rates were coupled with currency appreciation, asset price inflation, and strong movements in capital flows. If prior to the Global Financial Crisis (GFC), the ‘global saving glut’ hypothesis (i.e. Asian savings flight to the US) was one of the major effects resulting in booming US house prices, it is clear that a reversal effect has dominated the economy after the GFC: funds flight to Asia and the Pacific region putting pressure on asset prices, leading to financial vulnerability.",2.0,https://www.adb.org/sites/default/files/publication/220146/adbi-wp630.pdf
47503a540d26e6971c754bdcd066af61d3606fbc,10.21153/dpibe2013vol6no0art44,Marriage and Mortgages,2013.0,,Curtis Crawford; Y. F. Low; A. Rinaldo,"Using annualised data, this paper uses a regression model to help explain the relationship between home ownership, marriage, and key economic variables. By investigating the current body of knowledge this paper highlights support for two main theories that explain a fall in home ownership among those aged less than 35. Social trends, particularly delays in relationship formation (marriages), are cited by many academics as being the main reason for the decline in ownership among the young. However, there is more recent support for economic factors, suggesting affordability is much more of an issue for those who wish to achieve home ownership. The regression model, which uses home finance as a proxy for entry to home ownership, suggests that there is a statistically significant relationship between marriages and homeownership from 1979 - 2010 but not between median house prices and home ownership.",2.0,https://ojs.deakin.edu.au/index.php/dpibe/article/download/44/47
122e07c191e40cdc3321c3817fb731f2d55466e1,10.3846/ijspm.2023.20376,"PATH SELECTION OF SPATIAL ECONOMETRIC MODEL FOR MASS APPRAISAL OF REAL ESTATE: EVIDENCE FROM YINCHUAN, CHINA",2023.0,International Journal of Strategic Property Management,Yu Zhao; Xuejia Shen; Jian Ma; Miao Yu,"Urbanization, national economic growth, and China’s changing population structure have elevated the importance of real estate assessment in various contexts, including mortgage financing, secondary housing market transactions, and real estate tax reform. To address this need, this study employs a time-spatial double-fixed spatial cross-section data model as a mass appraisal tool to analyze the transaction price data of 429 ordinary residential houses in Xixia District, Yinchuan, China on April 1, 2022. Specifically, this study analyzes 7 spatial cross-section data models, discerning their interconnections. It devises an assignment technique that merges distance and characteristic variable rank into a unified indicator. The results explore spatial lag effects in real estate transaction price generation and assess the descriptive capabilities of different spatial cross-section data models.",2.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/20376/12003
3ea6239298be3f127372796473c81945b087a28e,10.1371/journal.pone.0341026,Exploring predictive models to improve the accuracy of Housing Price Index forecasts in India’s real estate sector,2026.0,PLoS ONE,Nutan Singh; K. Shanmugam,"This study aims to improve the predictive accuracy of the Housing Price Index (HPI) in India using the unrestricted MIDAS (U-MIDAS) model with novel predictors, including total digital payment value (TDP), consumer price index (CPI), and financial stress index (FSI). Quarterly HPI data from the Reserve Bank of India (RBI) and monthly data for other variables were used from October 2019 to September 2024. Correlation analysis indicates that HPI and digital payments are positively correlated. There is a weak relationship between HPI and FSI. HPI and CPI have a negative relationship. The U-MIDAS model outperformed other models. The study highlights the effectiveness of the U-MIDAS methodology for short-term forecasting. The study further demonstrates that the forecast accuracy obtained with the unrestricted mixed data sampling (U-MIDAS) regression exceeds that of the ARIMAX model.",2.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0341026&type=printable
b6c5ee38b07ef993439806dc524301bc79a6888d,10.1080/10549811.2025.2513228,Natural Attraction and Driving Forces: Analyzing the Impact of Parkland on Residential Prices and Typological Difference*,2025.0,Journal of Sustainable Forestry,Ruo-Nan Han; Siyu Wu; Fang He; Qiao-Yun Sun,"ABSTRACT Exploring the driving mechanism of parks and green spaces on the spatial heterogeneity of housing prices helps to rationalize the allocation pattern of social and environmental resources and the housing economic market, and to optimize the spatial pattern of green facilities in human habitat. The study integrates Shenzhen housing samples and their price transaction data, park green space and other related facilities data, and by using Ordinary Least Squares (OLS) Geographically Weighted Regression Model (GWR), compares and analyses the sequence of effects of architectural characteristics, location characteristics, neighborhood characteristics and green space characteristics on housing prices, and analyses the relationship between park green space proximity and housing prices, and the types of differences in their effect mechanisms. The results show that the driving effect of overall park green space proximity on housing prices shows the concentration effect of high-level development areas; the proximity of different types of park green space drives housing prices as urban parks > natural parks > community parks, and urban parks have higher economic value. The results of th",2.0,
71883a3bd2a3f689987eff1aeec41fbc6fe05aea,10.4028/www.scientific.net/amm.368-370.1946,Environmental Characteristics of a Residential Property on its Market Value,2013.0,,N. Sheng; Wa U. Tang; Cheng-Kun Liu; Hai-Ling Liu; Chao Xu; Jun Chang,"This study examines the environmental effects on residential property values in an extremely compact city of Macao Peninsula. Results showed that people would be willing to pay more for apartment with sea view and proximity to public recreational park. The availability of sea view for an apartment would increase the sale price by approximately 20%. About 5% increase in price would be observed if the apartment was located within 200m to public recreational park. Proximity to sewage treatment plant had a negative influence on the apartment price. About 9% price drop would be observed if the apartment was within 200m to sewage treatment plant. In addition, the sale price of an apartment would increase by about 5% if it is close to a trunk road with high traffic volume, which implied that people living in an extremely compact city tended to sacrifice serenity for convenience.",2.0,https://doi.org/10.4028/www.scientific.net/amm.368-370.1946
6336e4b1644e924ae2551baabd56375635e60a4f,10.1177/23998083221140884,Analysing the consequences of progressive Urban Spatial cycles to evaluate urban land use policy,2022.0,Environment and Planning B Urban Analytics and City Science,B. Mondal,"Urban expansion patterns have always evolved with various successive Urban Spatial Cycles (USCs). Both densification and dispersion strategies have shown limited success in optimizing local benefits such as affordable housing, access to amenities, a good environment, and low-cost transport in the cities of developing countries. However, in such countries, a systematic assessment of USCs and strategic utilization of the analyses, are not usually part of the land use policy-making process. The outcomes of the policy decisions are therefore less than optimal. This paper aims to explore the impact of progressive USCs on housing price, travel time, fuel consumption and environment. Based on geospatial data, this study examines the progress of USCs in Ahmedabad city. Landscape matrices and local perceptions are used to quantify the consequences of USCs using the Generalized Additive Model (GAM). Ahmedabad’s case study shows that the densification cycle is firmly active in the inner suburb and is closely associated with housing price inflation as well as the increase in travel time and fuel consumption. The cycles of fragmentation and sprawl in the outer suburban area are consistent with ",2.0,
a96d247980b0840c21f5095c2f6fc9fa55f2e9cc,10.22452/ijps.vol8no1.2,"Classification Modelling for the Impact of Historical Site on Residential Property Rental Value in Osogbo, Nigeria",2018.0,International Journal of Property Sciences,Oyedeji Oyedeji; Joseph Oyewale,"Previous studies have revealed that historical sites have impact on property value. However, none of the previous studies have forecasted the impact of historical site on residential property value using a classification model. Data for the study were gathered from the record of recent letting in the study area. For the purpose of precision, this study adopted artificial neural network, logistic regression and support vector machine as model of classifying the rental value of residential property in Osogbo, Nigeria. The study considered relevant variables which include distance to cultural site, age of building, state of exterior and state of interior as input variables. Findings from the study revealed that the three adopted forecasting models have over 80% percent of the forecasted properties correctly classified which make the property forecasting reliable.",2.0,https://ijps.um.edu.my/article/download/13166/8333
3a4ebbe1ecf07d053999e5dc66b45a5d9bd38465,10.3390/e25020249,Robust Variable Selection with Exponential Squared Loss for the Spatial Durbin Model,2023.0,Entropy,Zhongyang Liu; Yunquan Song; Yi Cheng,"With the continuous application of spatial dependent data in various fields, spatial econometric models have attracted more and more attention. In this paper, a robust variable selection method based on exponential squared loss and adaptive lasso is proposed for the spatial Durbin model. Under mild conditions, we establish the asymptotic and “Oracle” properties of the proposed estimator. However, in model solving, nonconvex and nondifferentiable programming problems bring challenges to solving algorithms. To solve this problem effectively, we design a BCD algorithm and give a DC decomposition of the exponential squared loss. Numerical simulation results show that the method is more robust and accurate than existing variable selection methods when noise is present. In addition, we also apply the model to the 1978 housing price dataset in the Baltimore area.",2.0,https://www.mdpi.com/1099-4300/25/2/249/pdf?version=1675331952
a8e9cd510fe3cb74707fd9a1a81f13a7c9f64ab5,10.1109/aiotc63215.2024.10748329,A Edge-Guided Satellite Image Semantic Segmentation Method for Real Estate Appraisal,2024.0,"2024 3rd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC)",Yinuo Cui; Yi-Lin He; Fang-Yuan Zhu,"This paper proposes an edge-guided satellite image semantic segmentation method for real estate appraisal. The digitization of real estate appraisal has been significantly enhanced by advancements in computer vision technologies, enabling automated recognition and analysis of property features from satellite images, aerial photographs, and street view images. However, current methods face limitations due to the resolution and quality of satellite imagery, which affect the precision of evaluations. To address these challenges, this study integrates semantic segmentation with a mathematical house pricing valuation model. The Edge Graph Attention U-Net is introduced, focusing on the edges and details of property features to enhance seg- mentation precision. The proposed semantic valuation algorithm integrates segmentation results with traditional market data and Geographic Information System data, employing mathematical model for property value prediction. Experimental results demon- strate the efficacy and accuracy of the proposed model across multiple cities and property types, providing a robust solution for modern real estate appraisal. The primary contributions include improved e",2.0,
b740022e42110567bf68fd05ede8cc53af8f9469,10.1109/raeeucci61380.2024.10547965,House Price Precision Assessor,2024.0,"2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI)",Siddarth Daga; Vishnu Priya Kulukuri; Teja sri Kosuru; M. Srilekha,"House price prediction using machine learning entails the development of a model capable of estimating the market value of residential properties by considering various features, including size, location, number of bedrooms, and more. This technology offers practical applications in the real estate industry, assisting buyers, sellers, and investors in making well-informed decisions. Machine learning algorithms analyze historical data to provide accurate price predictions, thereby facilitating property valuation and informed investment strategies. For a number of stakeholders, including investors, legislators, real estate brokers, and homeowners, the ability to anticipate property values accurately is crucial. A thorough summary of the methods and strategies used to forecast home values is provided in this abstract, along with important conclusions and new information from recent research. The study examines the wide variety of variables that affect home values, such as market trends, economic indicators, size, amenities, and location. Many machine learning approaches, such as neural networks, decision trees, random forests, and linear regression, have been utilized to build predict",2.0,
917671a3b29d3cfb8a684377ac6726fb693468fd,10.1109/itc-cscc58803.2023.10212833,Predict Condominium Prices in Bangkok Based on Ensemble Learning Algorithm with various factors,2023.0,"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)",Thanit Anchaleechamaikorn; Taninnuch Lamjiak; Thagoon Thongpe; Lapis Thiralertpanit; Jumpol Polvichai,"Real estate valuation is a critical process that determines the monetary value of a property. Three common approaches to property valuation are the sales comparison approach, the income approach, and the cost approach. However, these methods can be time-consuming, costly, and may suffer from human bias. This paper proposes an Artificial Intelligence (AI)-based system to evaluate the price of condominiums faster, more cost-effectively, and without human bias. We present a comprehensive methodology that utilizes web scraping and machine learning techniques to predict condominium prices accurately. The dataset is obtained through Python scripts that extract information on all condominiums listed on a popular Thai property website. The collected data covers appraisal years from 2008 to 2019. The AI models used in this study include linear regression, Random Forest Regressor, Gradient Boosting Regressor, and XGBoost Regressor models. We evaluated the performance of these models using the MAPE metric. Our results show that machine learning models can predict condominium prices accurately, providing valuable insights into the real estate market in Bangkok. This research contributes to the",2.0,
cb6b998db6d3c4290c51ee5d68cd4d1e2691f197,10.1109/icaiss58487.2023.10250452,Random Forest-based House Price Prediction,2023.0,2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS),B. Sri; C. R. K. Reddy; Chiluveri Rithish Kumar; Akula Vyshnavi; Buttagalla Vinod; B. K. Reddy,"The precise prediction of house prices is highly significant for professionals in the real estate industry, homeowners, and potential buyers. This study primarily focuses on utilizing the random forest algorithm to accurately predict house prices. A comprehensive dataset containing various features such as location, size, rooms, amenities, and historical transaction data was collected. The investigation included steps of data conditioning, designing features, instruction of models, and oversight. To assess the outcome of the model along with avoiding the situation a random-forest stance was employed, and validated cross-valid techniques were employed. Lasso regression is employed to identify the key features that significantly influence house prices. Results showed promising predictive accuracy, surpassing other baseline algorithms. Feature importance analysis highlighted the significant influence of location, size, and the number of rooms on house prices, aligning with domain knowledge. This study illustrates the precision of random forests and Lasso-regression-based ML assertions, assisting housing specialists, homeowners, and potential purchasers in making accurate choices. Futu",2.0,
d307e4227aaa450448cc6f92c009611073ab61ef,10.1016/j.heliyon.2023.e23469,A study on the impact of housing prices on residents' travel frequency and transportation resilience in 35 Chinese cities,2023.0,Heliyon,Bi-Yu Peng; Jie Wang; Xiaohua Xia; Zhu Ma,"As urbanization proceeds, urban transportation resilience suffers greater challenges. Covering panel data for 35 Chinese cities from 2006 to 2019, after controlling for other variables that may affect urban residents' transportation volumes, this paper finds that rising houseing prices have a significant positive effect on urban residents' traffic frequency. As frequent traffic trips increase the probability of urban residents experiencing disruptions, rising housing purchase prices would have a negative impact on Chinese urban residents’ transportation resilience. The regional comparison analysis finds that the regression coefficients of coastal and eastern cities with more expensive housing price are significantly higher than those of non-coastal and western cities with lower housing price, further validating the findings of this paper.",2.0,https://doi.org/10.1016/j.heliyon.2023.e23469
33080dafde3c941219f91ca290c7a16156221f3a,10.1016/j.procs.2015.07.109,A Novel Dynamic Financial Conditions Index Approach Based on Accurate Online Support Vector Regression,2015.0,International Conference on Information Technology and Quantitative Management,Xiang-Yu Hua; Zhi-Min Yang; Ya-Fen Ye; Yuan-Hai Shao,"Abstract In this paper, we construct a novel dynamic financial conditions index (DFCI) for China based on accurate online support vector regression (AOSVR), and the constructed DFCI is evaluated on future inflationary pressures. The research results indicate dynamic effect of financial variables on DFCI in time-varying economic and financial environment, verifying the dynamic nature of the weights in our DFCI. On the whole, in our DFCI exchange rate, stock price, and money supply have the push-down effect on DFCI, taking negative dynamic weights. Housing price has the pull-up effect on DFCI, taking positive dynamic weights. The effect of interest rate on DFCI is erratic, taking sign-changed dynamic weights. The Granger causality test results show the superior performance ability of our DFCI compared with the FCI constructed based on SVR.",2.0,https://doi.org/10.1016/j.procs.2015.07.109
821975318999a19182bad9230a88ef0b43d45980,10.35136/krer.32.3.1,Analysis of the Relationship between Housing Prices and Regional Characteristics Using Machine Learning and XAI,2022.0,Korea Real Estate Institute,Geonhee Yang; Hae-jung Chun,"This study aims to explain the relationship between variables that were not explained in the existing machine learning methodology by interpreting the relationship between apartment prices and regional characteristic variables using the XAI methodology. Furthermore, the study's goal is to collect various regional variables (population, occupation, income/consumption) in the basic area unit (postcode unit) that could not be addressed in previous domestic studies due to data collection difficulties. In terms of the independent variable, a dataset was created for each facility, population, occupation, income/consumption, and the dependent variable. The relationship between independent and dependent variables was inferred using a random forest among XGBoost and Bagging models and SHAP models among XAI methodologies on the constructed dataset. The spatial scope of the analysis was determined by dividing Seoul Metropolitan Government into national basic zone units, the content range was apartment among houses, and the period was empirically analyzed in 2021. Results reveal that the proportion of high income, the proportion of rental income, and the number of apartments positively affect ",2.0,
9744b3fa9eeeb19d80246fb6abab550b0276ccb0,10.54254/2755-2721/49/20241058,House price prediction based on different models of machine learning,2024.0,Applied and Computational Engineering,Chuhan Ni,"Housing price prediction is a typical regression problem in machine learning. Common algorithms include linear regression, support vector regression, random forest, and extreme gradient boosting models based on integrated learning methods. Among the specific problems, different models in the specific problem will get different results. This research will compare these three models to show which model is more accurate and robust. Given the practical problem of housing price prediction, various characteristics of houses are carried out. The research will analyze and study, apply a variety of regression models, and compare the performance of the above three models on this problem, make the horizontal comparison of the advantages and disadvantages of different models, and analyze the difference in effect Line analysis and summary.",2.0,https://www.ewadirect.com/proceedings/ace/article/view/10770/pdf
da89e9dc1883472073149b34d9613798ec4b83d6,10.1111/manc.12397,"Revisiting the determinants of house prices in China’s megacities: Cross‐sectional heterogeneity, interdependencies and spillovers",2022.0,Manchester School,Chun-Ping Liu; Zhi-Rong Ou,"We revisit the determinants of house prices in China(cid:146)s megacities. Previous work on similar topics fails to account for the widespread cross-sectional heterogeneity and interdependencies, despite the importance of them. Using a PVAR estimated by the Bayesian method allowing for these features, we (cid:133)nd each city is rather unique, especially on the extent to which local house prices are disturbed by external house price shocks. The spillovers are mainly due to direct housing market interdependence, which seems related more to demand before 2010, but more to supply thereafter due to property purchase restrictions. The new evidence we establish therefore suggests that city-level stabilisation of house prices should fully respect local features, including how local markets respond to external disturbances.",2.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/manc.12397
6df251147348482e626686a7af688e852f8411f7,10.24843/jim.2024.v12.i01.p06,"Comparison Of Decision Tree, Linear Regression, and Random Forest Regressor Models for Predicting House Prices",2024.0,Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi),Ni Putu Sutramiani,"A home is a basic requirement that offers comfort and security to its occupants. Because they are subject to price fluctuations, houses are also a potential option in an investing setting. As a result, buyers and investors require a system that can forecast house values. This study compares the effectiveness of decision trees, linear regression, and random forest regressors as models for predicting home prices. The dataset for predicting home prices was used in this study to conduct data exploration, pre-processing, modeling, and model comparison stages. The study's findings demonstrate that the random forest regressor offers the best prediction performance with lower assessment metrics, including MAE, MSE, RMSE, and R2 Score, making it the best option for predicting house prices and other financial outcomes.",2.0,https://ojs.unud.ac.id/index.php/merpati/article/download/112815/55168
4929d4c428d9ac80ed4774b28011b14a7feb576d,10.4028/www.scientific.net/amr.798-799.885,Influence of Modeling Methods for Housing Price Forecasting,2013.0,,Xiao-Li Yang; Qiong He,"In this work, we estimate Yunnan housing price from 1999 to 2009. Firstly, we analyze the correlation coefficients between housing price and characteristic variables, identify the characteristic variables. Then, we build the forecasting model using four techniques, support vector regression (SVR), radial basis function neural network (RBFNN), partial least square (PLS) and multiple regression analysis (MRA), based on whole variables and characteristic variables. The results show that PLS technique is the best one for housing price forecasting. Its mean absolute percentage error (MAPE) is only 2.45%. SVR and RBFNN are better techniques to obtain a satisfactory forecasting result with almost 5% MAPE. Furthermore, the performance of MRA and SVR can be obviously improved through variables selection.",2.0,https://doi.org/10.4028/www.scientific.net/amr.798-799.885
de85e492d4c71ede92f719c2ae2ac073f4282233,10.22214/ijraset.2025.72962,Real Estate Price Prediction Using Machine Learning Models,2025.0,International Journal for Research in Applied Science and Engineering Technology,Sairaj Nagula,"Accurate real estate price prediction is crucial in today’s market to aid buyers, sellers, and investors in making
informed decisions. This study employs machine learning algorithms—specifically Linear Regression, Decision Tree Regression,
and Random Forest Regression—to model and predict housing prices based on various influential features. The methodology
involves data preprocessing, feature engineering, and model evaluation using standard metrics like R² score and Root Mean
Squared Error (RMSE). The models are trained on real-world housing datasets, and results demonstrate the efficiency of
ensemble learning over traditional linear approaches. This paper establishes that Random Forest offers the most accurate
predictions and is suitable for practical applications in real estate.",2.0,https://doi.org/10.22214/ijraset.2025.72962
8b99b7c0a65b9b01610eb3d2cf2f54527a64cfe4,10.48175/ijarsct-22501,Housing Price Prediction using Linear Regression,2024.0,"International Journal of Advanced Research in Science, Communication and Technology",Abhimanyu Singh; U. Sree R,"In today's world, real estate is a major investment, particularly in a city like Mumbai, which is a dream destination for many to work and settle down. Hence, understanding the real-time value of a property is crucial before investing your hard-earned money. The primary The purpose of this essay is to estimate the current market price of homes in Mumbai. Various factors, like the quantity of bedrooms and the availability of different amenities, are considered in this prediction. This estimation is designed to assist customers in finding suitable options that meet their needs. We have utilized the Linear Regression model to forecast the prices of the homes in question. This model minimizes the necessity of consulting a broker, thus providing additional assistance to the customer.",2.0,https://doi.org/10.48175/ijarsct-22501
ecd7aa66cb2d68f86b720e7987559e9a8c0754df,10.1080/07352166.2020.1860677,Typology of home value change over time: Growth mixture models in Southern California neighborhoods from 1960 to 2010,2021.0,Journal of Urban Affairs,John R. Hipp,"ABSTRACT This study uses U.S. Census data on average home values in Southern California census tracts from 1960 to 2010. Using growth mixture modeling (GMM), 26 unique groups are detected capturing nonlinear change in neighborhood relative home values over this study period. There were seven broad patterns of changing home values: (1–3) decline and then rise (at high, mid, and low portions of the home value distribution); (4) rise and then decline; (5–6) a monotonic increase (either above or below the region average); and (7) a monotonic decrease. Multinomial regression models found that covariates exhibited a much stronger effect for distinguishing between the average level of home values in neighborhoods over the study period, rather than how home values changed over time.",2.0,https://escholarship.org/content/qt8c35p3df/qt8c35p3df.pdf?t=r07kwv
f06c25ac3797097edbfba0499d0151eff5f6870d,10.1109/prmvia58252.2023.00028,A Comparative Study Of CART Algorithm For Forecasting,2023.0,"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)",Juan Yan; Quan Zhou; Ya Xiao; Bin Pan,"CART algorithm is a tree structure used for classification rules in the form of decision tree from a group of unordered and irregular cases. Compared with other classification methods, it has the advantage that a busy large amount of data can is classified yen fully, and then valuable potential information can be found. The method is simple and intuitive, with fast classification speed and high accuracy, which is suitable for large-scale data processing. Moreover, the algorithm process is easy to understand and can though express the importance of attributes praying attributes. The significant sensitivity and unpredictability of house price make it difficult to construct its forecasting model. In this paper, through an example of house price, the influencing factors of house price are deeply analyzed and the existing research results are systematically sorted out, and the decision tree CART detailed is used to build a molybdenum metal price algorithm model and forecast the actual price. By comparing and analyzing the results by using Not principles, the average absolute error is 4.03%, and the accuracy rate of foreforetrend forecasting trend can reach 94.8%, which shows that the al",2.0,
fc2f6eafea321324e89cf37e787a95cea0703b75,10.53730/ijhs.v6ns3.8255,Machine learning based prediction of house price,2022.0,International Journal of Health Sciences,T. Suganya; M. Gowtham; H. Raja,"Getting a house of our wishes within our budget in a residential area of our customization is quite a tedious process. In order to overcome this, we have developed a model to get a houses of our interest with religious belief and budget this Implemented model is of linear regression and k nearest neighbor’s algorithm with gradient descent optimization to make an optimal model for predicting house prices using the dataset. Performed feature engineering and selection using lasso and ridge penalties to eliminate features which had little or no impact on the residual sum of squares error. Then exposes Jupyter notebook cells as REST Endpoints to make prediction with new information. Finally, we are trying to send email alert to the concern user to give alert of the house price",2.0,https://sciencescholar.us/journal/index.php/ijhs/article/download/8255/4853
6f747b37298399e30fbe9770da4cc1119260f422,10.2139/ssrn.1873898,The Relevance of Amenities and Agglomeration for Dutch Housing Prices,2011.0,Social Science Research Network,H. Garretsen; G. Marlet,"In this paper we have combined concepts from the field of urban economics with views from the area of geographic economics (the New Economic Geography). This approach enabled us to depict both the significance of the characteristics of the city itself and that of its location. Cities which combine a favourable location in terms of distance to work with a variety of urban amenities appear to be the most attractive locations for people to live. These are relatively safe cities, offering a variety of history and culture events, as well as good restaurants. In addition, successful cities are places where people can optimize their career prospects, not necessarily – as often assumed – as a result of business districts in these cities, but access to jobs from these cities.",2.0,https://doi.org/10.2139/ssrn.1873898
d1b33ae8fda9128b5ad5e719016784688fd87172,10.22389/0016-7126-2020-965-11-40-49,Method of market data extrapolation in time for mass appraisal of land under conditions of weak developed market,2020.0,Geodesy and Cartography,E. Bykowa; J. Volkova; M. A. Sulin; A. Morozov; K. E. Shemelina,"
The matter of cadastral estimation results is one of the most topical in Russia nowadays. People do not agree to fi scal burdens, scientists are not satisfi ed with mass estimation institutions in general. The main reason for such a situation is problems with source data, namely their insuffi cient quality and quantity. As for insuffi cient quality, the only way to make source data better is proper collecting and storing process, but for quantity the way is to develop new methods of mass estimation. The research is devoted to method of market data extrapolation in time for mass assessment of land development, which is effective under conditions of a weak-developed market. This analytical statistical approach is based on implementation of time factor into multiple regression equation that enables using market data extrapolated in time. The method consists of such steps as collecting price forming factors and, sales data over the period of 10 years, making thematic maps, adequacy analysis, and constructing a valuation model. The outcome of the method implementation in cadastral estimation of individual residential development land plots in Votkinsk city showed that it is 50 % more e",2.0,
8b4130b30360630b9f6359917bc5a9117459b657,10.22214/ijraset.2023.56935,Effective Machine Learning Algorithm to Predict House Price,2023.0,International Journal for Research in Applied Science and Engineering Technology,Ashwani Kumar; Hemadri Rawat; Yamini Singh,"Abstract: The real estate industry, known for its dynamic pricing, is a prime candidate for the application of machine learning to enhance and accurately predict property costs. This paper focuses on predicting the market value of real estate properties, specifically in Mumbai, using a Decision Tree Regressor. The system leverages geographical variables, analyzing past market trends and upcoming developments to forecast future prices. The research aims to empower users to make informed investment decisions without relying on intermediaries. Results demonstrate an 89% accuracy with the Decision Tree Regressor. The paper discusses the system's methodology, emphasizing data preprocessing, model training, and integration with Flask for a userfriendly interface.",2.0,http://dx.doi.org/10.22214/ijraset.2023.56935
95e5e3dfaec53f362b2138f7de10b650d2878aa0,10.1108/jerer-04-2024-0023,Impact of inflation on residential property prices in the V4 countries,2025.0,Journal of European Real Estate Research,O. Crnadak; Adam Černohorský; N. Petrů,"PurposeReal estate prices can have a significant impact on the emergence of economic crises. Our study focuses on the analysis of the impact of inflation on residential property prices in the Visegrad Four (V4) countries (the Czech Republic, Poland, Slovakia and Hungary). The paper’s objective is to verify and compare the impact of inflation on residential property prices.Design/methodology/approachData are provided by the European Central Bank and Organisation for Economic Cooperation and Development (OECD) for each quarterly period from Q1-2008 to Q3-2023. For data analysis, Pearson’s correlation coefficient and linear regression using the OLS method have been used.FindingsThe results indicate that there exists a positive correlation between residential property prices and inflation for all V4 countries. Linear regression has proved that residential property prices in all V4 countries are positively impacted by inflation. Contrastingly, our results indicate that for all V4 countries, inflation is not sufficient to define a statistically robust regression model that can explain and predict residential property prices.Originality/valueSimilar studies have been done in the OECD and ",2.0,
7d61981b386e99be54d31f175da6b9213f60f5c7,10.1080/10527001.2022.2111749,Adjacency Neighborhood Effects and Owner-Assessed Home Values,2022.0,Journal of Housing Research,J. Freybote; Lauren Simon; Akhila Veldandi,"Adjacency neighborhood effects such as neighbor interactions or perceived neighborhood quality have been neglected in the housing literature due to a lack of data. We investigate the impact of these effects on the housing wealth perceived by homeowners. Using a national longitudinal survey, we find that perceived neighborhood quality positively impacts owner-assessed home values. However, this effect is driven by homeowners ’ assessment of their neighborhoods ’ quality relative to other neighborhoods rather than their neighborhoods ’ perceived cleanliness or upkeep. Depending on the financial characteristics of homeowners, we also find neighbor interactions and perceived neighborhood safety to have a significant relation with self-assessed home values. Trust and support among neighbors and joint feel of the home and neighborhood on the other hand have no relation with estimated values. While neighborhood quality consistently explains owner-assessed values, home quality only has a relation with value estimates for homeowners with certain financial characteristics. Considering the importance of perceived housing wealth for household financial decisions as well as transaction prices, ",2.0,
bb028e30ca2293e10672f2b668c4ba279584ece7,10.1093/jrsssc/qlaf020,Modelling time-varying relations in housing prices: a semiparametric panel approach,2025.0,"Journal of the Royal Statistical Society, Series C: Applied Statistics",Marina Friedrich; Yi-Cong Lin; Pavitram Ramdaras; S. Telg; Bernhard van der Sluis,"
 For the modelling of housing prices, housing-specific characteristics play a key role while external economic conditions are often omitted. We introduce a semi-parametric panel model with time-varying coefficients, incorporating housing-specific attributes, as well as both observed and unobserved external economic conditions. For a monthly data set spanning from 2006 to 2020 on 60 Dutch municipalities, our model accurately captures comovements in highly nonlinear housing price series, revealing significant variations in the relation over time. We find that housing characteristics and the external environment explain comparably high proportions of variation in the prices, which emphasizes that both factors should be included in empirical analyses.",2.0,https://doi.org/10.1093/jrsssc/qlaf020
8329139cbfeb35556cbf7eefe4b80e381a9627f5,10.1109/icccnt61001.2024.10726166,Enhanced House Price Prediction Using Machine Learning Techniques,2024.0,International Conference on Computing Communication and Networking Technologies,A. Verma; Durgesh Singh; Netra S. Patil; Sagar G. Mohite; Sakshi Ranjan; M. Raj,"Real estate is a crucial part of the global economy, contributing significantly to financial stability. A person’s decision to purchase or sell a home is a critical financial matter. Consequently, accurate house price predictions are essential. This research paper utilizes data and advanced machine-learning techniques to develop a robust and precise model for predicting house prices. The model provides valuable insights into the dynamic real estate market by examining numerous relevant attributes, including bathroom size and real-time economic data. Techniques such as Linear Regression, Decision Tree regression, and Lasso Regression were employed to make accurate price predictions-the proposed research utilized outlier removal, dimensional reduction, and feature engineering to enhance model performance. The novelty of this work lies in integrating detailed features and real-time scenario analysis, addressing gaps in existing studies. Finally, we deployed the model as a user-friendly web application on an AWS EC2 instance, allowing users to estimate property prices and make informed decisions conveniently. This comprehensive approach builds on previous research and significantly imp",2.0,
7c7e089d4656c5ff0da78038f60d6300349c8d54,10.2139/ssrn.3612520,Housing Cycle and Firm Investment: International Firm-level Evidence,2020.0,Social Science Research Network,Hyunduk Suh; Jin-Young Yang,"We analyze international firm-level data to examine the relationship between housing cycle and firm capital expenditure or R&D spending. We use the housing price component independent of firms’ investment opportunity and credit supply shocks, obtained by historical decomposition of a structural VAR, as the key explanatory variable. The baseline results support the existence of the collateral channel as housing price growth and firm investment exhibit a positive relationship. This collateral channel is more distinct for capital expenditure than R&D spending, and in housing market downturns than booms. Another notable finding is that despite the collateral channel, large housing price booms are detrimental to investment, which suggests a possible reallocation of resources from the production sector to the housing sector during those phases. Moreover, various firm-specific and country-specific characteristics are found to affect the housing price-investment relationship. Small firms and firms with stronger investment opportunities respond more sensitively to housing price shocks. Countries that rely more on bank financing, collateralized lending, and with higher LTV restraint, displ",2.0,
25220943ab063e0fe03168460d2005c02cf25de0,10.2991/icetis-13.2013.254,Mass Appraisal of Property Tax Base based on Multivariate Linear Regression—Empirical Study on Four Districts of Jinan,2013.0,,Shan Tian,"In this paper, a multivariate linear regression analysis is made to the effective transaction data of 226 housing stocks in four administrative regions (Tianqiao District, Shizhong District, Lixia District, and Huaiyin District) of Jinan, a mass appraisal model of real estate values is established, and also the transaction prices of 226 housing stocks are compared with the appraised prices. In this study, it is thought that an efficient, low-cost, objective, and accurate mass appraisal can be made to property tax base through the establishment of a unified real estate information database. In the mass appraisal system, geography information technology should be integrated so as to improve the objectivity and accuracy of the appraisal results.",2.0,https://www.atlantis-press.com/article/8103.pdf
2f38af04afe4319f348d3789afbeb7c5b267c6e9,10.1117/12.2656904,Housing price forecast based on XGBoost algorithm house price: advanced regression techniques,2023.0,"Conference on Statistics, Data Science, and Computational Intelligence",Chutong Xu,"House prices have always been a major concern in people's daily lives, not only for individuals, but also for the stability of society as a whole. Accurate prediction of house prices can help people to buy houses with a great degree of reference and help the state and society to control the overall consumer prices. With the development of machine learning, we are able to make greater use of machine learning tools to obtain accurate forecasting of house prices in recent years, in order to attempt to predict the house price, this article utilize the machine learning algorithms. The house price problem is a typical regression problem in machine learning. In this paper, we obtain data from Kaggle, look at the data and find that there are many factors affecting house prices. By pre-processing the original data, engineering and standardizing the features, finally, machine learning algorithm XGBoost is selected to establish a prediction model for housing price prediction. We selected the root mean square error (RMSE) ranging from the logarithm of the expected value to the logarithm of the real selling price as the final result of the forecast. The final results we obtained can help one to",2.0,
c0ccfc24ab44ae1818f22e04a8d24eeeb2cb325e,10.5430/afr.v8n2p99,Considerations for a Regression-Based Real Estate Valuation and Appraisal Model: A Pilot Study,2019.0,Accounting and Finance Research,Shawn L. Robey; Mark A. McKnight; M. R. Price; R. Coleman,"This paper advocates a more scientific approach to residential real estate valuation as opposed to more traditional approaches, which are flawed for two main reasons: (1) appraiser judgements are almost exclusively used and (2) appraisers’ sample sizes are too small to provide adequate estimated values. By using a regression model, this paper explores the impacts of different characteristics on market value. Three hundred and fifteen properties in Evansville, Indiana, were analyzed testing twelve different variables. This model suggests that 91.8% of the total market value variation is explained by four independent variables. These findings provide evidence that multiple linear regression could be used to better predict a property’s value.",2.0,http://www.sciedu.ca/journal/index.php/afr/article/download/14978/9485
164cbfb574c942f5224d4e5678d223fc3c998197,10.31284/j.kernel.2024.v5i2.6806,PREDIKSI HARGA RUMAH DI JABODETABEK MENGGUNAKAN METODE ARTIFICIAL NEURAL NETWORK,2025.0,KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika,M. Hafizh; Subairi Subairi; Raditya Dimas Libriawan; Naufal Maulana; A. Rizki,"A house is a fundamental need for humans. Determining house prices is a crucial aspect of property transactions, especially in major areas like Jabodetabek, where property prices are consistently rising. Prediction is a suitable tool to assist in decision-making for determining house prices. There are numerous methods that can be applied for prediction; the author employs the Artificial Neural Network (ANN) method. ANN is known as a highly flexible predictive algorithm capable of accommodating various input features. The results of using the ANN method for predicting house prices in the Jabodetabek area show a Mean Absolute Error (MAE) of 0.209, Mean Squared Error (MSE) of 0.159, and Mean Absolute Percentage Error (MAPE) of 4.951.",2.0,https://ejurnal.itats.ac.id/kernel/article/download/6806/4363
7c1042d804dc05284709a441fe214921128b7cb9,10.24294/ijmss.v6i4.3213,Comparison of Ridge Regression and GA-RF Models for Boston House Price Prediction,2023.0,International Journal of Mathematics and Systems Science,Liang Ye,"The purpose of this paper is to explore the performance of ridge regression and the random forest model improved by genetic algorithm in predicting the Boston house price data set and conduct a comparative analysis. To achieve it, the data is divided into training set and test set according to the ratio of 70-30. The RidgeCV library is used to select the best regularization parameter for the Ridge regression model, and for the random forest model, the genetic algorithm is used to optimize the model's hyperparameters. The result shows that compared with ridge regression, the random forest model improved by genetic algorithm can perform better in the regression problem of Boston house prices.",2.0,https://www.enpress-publisher.com/files/journals/31/articles/3213/public/3213-10517-1-PB.pdf
dd7018d575196a58c6946607606d354c3cc37699,10.1108/ijhma-11-2022-0170,The effect of social capital on housing prices: panel data analysis by province in Turkey,2023.0,International Journal of Housing Markets and Analysis,Abdulmuttalip Pilatin; Ali Hepşen; Onur Kayran,"
Purpose
This study aims to reveal whether social capital has an effect on the housing price index in Turkey, which is a developing country. The research was carried out by using the data on the basis of 81 provinces of Turkey in a 12-year period covering the years 2007–2018.


Design/methodology/approach
The data were subjected to panel data regression analysis and the related models were tested using the Driscoll-Kraay (1998) Estimator.


Findings
According to the results of the analysis, it was understood that there is a negative and significant relationship between social capital (SC1) and the housing price index. The results were corroborated by susceptibility testing. As the level of social capital rises in the provinces in Turkey, the manipulative and opportunistic behavior tendencies of individual and corporate house sellers decrease. These results support the principal–agent theory and theory of moral hazard, which constitute the theoretical background of the study.


Originality/value
No study has been found in the literature on the effect of social capital on housing prices. This situation constitutes the main motivation source of the study and shows its originality.
",2.0,
c637a826f8ccb62d18fe535adddef55c8e296b72,10.1080/17421772.2025.2603503,Causality in spatial economic analysis: with reference to the London Green Belt and house prices,2026.0,Spatial Economic Analysis,B. Fingleton,"ABSTRACT This paper illustrates possibilities and pitfalls of two different approaches to causal analysis, the Rubin causal framework (RCF) and spatial econometrics, namely the spatial Durbin model (SDM). The two are directly opposing, for unlike the RCF, spatial interaction is embodied within the SDM. The main innovatory aspects are an attempt to reconcile RCF and SDM via ATIVE, meaning the average treatment instrumental variable effect, which parallels the average treatment effect (ATE) based on regression adjustment (RA). Additionally the paper illustrates the efficacy of using synthetic instrumental variables, providing estimates of the impact of London’s Green Belt on house prices.",2.0,https://www.tandfonline.com/doi/pdf/10.1080/17421772.2025.2603503?needAccess=true
4372fba4ddb3750bd351dc7ce2d5b0a91afa968e,10.33423/jhetp.v19i2.1447,The Relationship Between Georgia Public School Educational Funding Sources and Academic Achievement,2019.0,Journal of Higher Education Theory and Practice,Rosalind Ray; Teresa M. Lao,"Revenues from local property taxes has been the source of public funding for schools in Georgia. This quantitative study evaluated financial management of public funding in local school districts and student academic achievement outcomes. The research examined whether there was a difference in achievement levels of economically disadvantaged students based on the 8th grade population, property valuations, and home prices from 2006 to 2014 school terms. Correlation analyses indicated the existence of an inverse relationship between the variables. Multiple regression analyses revealed that academic performance was predicted by the total number of 8th grade students who passed the test.",2.0,https://articlegateway.com/index.php/JHETP/article/download/1447/1377
bb590b0ebc4a6b4fb24c4985a07b52e6e428c0f5,10.17016/feds.2017.115,"Bank failures, capital buffers, and exposure to the housing market bubble",2017.0,Real Estate Economics,G. Kara; Cindy M. Vojtech,"We develop housing overvaluation measures that are separate from local economic conditions and show that banks with greater exposure to such overvalued markets have higher mortgage delinquency and charge‐off rates and significantly higher probabilities of failure during the 2007–2009 financial crisis even after controlling for bank characteristics. While high house prices relative to fundamentals present a greater likelihood of house price correction, we find no evidence that banks managed this risk by building capital. We also show that our overvaluation measures are important in explaining individual mortgage loan defaults and could be used to improve bank risk management.",2.0,https://doi.org/10.17016/feds.2017.115
64bcada8e378388c92bdc0ab3eb7a59366fa1b0a,10.1111/1540-6229.70033,Natural disasters and housing prices: What can we learn from tornadoes?,2026.0,Real Estate Economics,Jeffrey P. Cohen; Violeta A. Gutkowski,"The impact of tornadoes on housing prices has not been extensively explored in a causal analysis framework. We estimate the effects of damage from a major tornado in Little Rock, Arkansas, on prices of nearby undamaged homes. We study how a typical home's proximity to damaged properties might have led to a discount in its price due to severe blight in the neighborhood. We focus on homes that sold between January 2022 and August 2024 and compare the effects of the March 31, 2023, tornado on sale prices for homes near versus far from damaged properties. For all home sales within 250 m of at least one tornado‐damaged property, our difference‐in‐differences estimates imply an average discount of approximately 20% relative to home sales further away. These effects disappear with greater distance from the nearest damaged property. Second, homes in lower income census block groups did not incur price effects that were significantly different from the effects for other homes. Finally, we show that the impact was short‐lived on average, as we find no significant price discount on homes sold near or far from damaged properties 9 months after the tornado.",2.0,
91045fe72abab4e0956f750f2832fd3c113a3325,10.54941/ahfe1005725,A hybrid Regression method for Predicting Housing Prices,2024.0,AHFE International,Gaurab Baral; Junxiu Zhou,"Accurate house price prediction is crucial for accommodating the diverse needs of stakeholders in the home-buying process. House prices can be affected by various factors, such as location, construction date, exterior, etc. This work proposes a hybrid regression method that leverages the strengths of different regression techniques to improve prediction accuracy. Specifically, this work looks at conventional linear regression and other machine learning techniques such as support vector regression (SVR), and XGBoost regression. Then we compare these models with our proposed hybrid regression model that leverages ridge regression and lasso regression to capture hidden relationships between house properties and sale prices to reveal the different predictive power of these models. In addition, this work also highlights feature engineering to address potential issues in the data and improve prediction performance. The dataset used in this study is obtained from the Kaggle Competition “House Prices: Advanced Regression Techniques.” Different model results are submitted to Kaggle, and the scores are illustrated in the paper.",2.0,
cf589823ddd33522fd7b92a02e139297739e4036,10.2139/ssrn.3605798,Decision Tree and Boosting Techniques in Artificial Intelligence Based Automated Valuation Models (AI-AVM),2020.0,Social Science Research Network,T. Sing; Jing-Ye Yang; Shi-Ming Yu,"This paper develops an artificial intelligence-based automated valuation model (AI-AVM) using the decision tree and the boosting techniques to predict residential property prices in Singapore. We use more than 300,000 property transaction data from Singapore’s private residential property market for the period from 1995 to 2017 for the training of the AI-AVM models. The two tree-based AI-AVM models show superior performance over the traditional multiple regression analysis (MRA) model when predicting the property prices. We also extend the application of the AI-AVM to more homogenous public housing prices in Singapore, and the predictive performance remains robust. The boosting AI-AVM models that allow for inter-dependence structure in the decision trees is the best model that explains more than 88% of the variance in both private and public housing prices and keep the prediction errors to less than 6% for HDB and 9% for the private market. When subject the AI-AVM to the out-of-sample forecasting using the 2017-2019 testing property sale samples, the prediction errors remain within a narrow range of between 5% and 9%.",2.0,
96d445a3237b9dba056b01fbc4f5af6bdc1a81b8,10.24191/ji.v19i2.26615,Application of the ARIMA model in house price index in Malaysia,2024.0,Jurnal intelek,Muhammad Harith Ikhwan Zamri; Muhamad Afizi Rifin; Amit Norani,"The factors that affecting the escalating price of houses in Malaysia are driven by factors such as population growth, income dynamics, interest rates, and GDP. This phenomenon has notably outpaced the growth of household incomes, thus majorly impacting Malaysians. The study’s primary goal is to forecast the housing price index in Malaysia from the best model obtained using Box-Jenkins method. aligning with the 2018-2025 National Housing Policy objectives, utilizing advanced machine learning and time series modeling. The objectives guide the research: to find the best model for predicting house price index in Malaysia using Box-Jenkins Method. Utilizing secondary data from the National Property Information Centre (NAPIC) spanning from 1988 to 2023, the study employed the analytical method of ARIMA. The results favored the ARIMA (1,1,1) model as the best model in predicting housing price indexes. This offers an excellent forecasting model for residential properties towards gaining better understanding of their pricing dynamics and offers potential solutions to the issue of housing affordability for Malaysians.",2.0,
b9ba71953e18672e16d5de7e3209bcfe7c6d3286,10.1109/icpics58376.2023.10235649,Research on the Application of Integrated RG-LSTM Model in House Price Prediction,2023.0,"2023 IEEE 5th International Conference on Power, Intelligent Computing and Systems (ICPICS)",Guang Wang; Z. Shu,"Aiming at the problem that the traditional time series analysis method cannot fit the nonlinear components in the time series, and the conventional regression analysis cannot reflect the relationship between the time series, it is proposed to use the lag observation method to reconstruct the phase space of the multi-dimensional data, transform the time series problem into a supervised regression problem, and then use the stacking integration idea to establish a regression analysis and long-term and short-term memory network combination model (RG-LSTM), and use it for house price prediction. Firstly, based on the literature review method, the housing price index system is established; secondly, on this basis, the RG-LSTM model is applied to the prediction of housing prices in Nanjing, and comparative experiments are carried out. Finally, the experimental results show that the integrated RG-LSTM model has the advantages of high prediction accuracy and high reliability compared with the traditional time series prediction model, which indicates that the model has great advantages in predicting housing prices.",2.0,
068536806271ff0eb91065900cf4fb4bc2f4b233,10.1109/icpcsn62568.2024.00055,Exploring House Price Forecasting through Machine Learning and Data Preprocessing,2024.0,2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN),A. Vaishnavi; G. Raghavendra; Mohammed M. Jilan; A. P. Chowdary; Romen Singh; K. C,"Predicting house prices accurately is crucial in real estate, influencing decisions for buyers, sellers, and investors. Machine learning has emerged as a potent tool in this domain, leveraging historical sales data, property features, and economic indicators to forecast future prices. However, the efficacy of machine learning models hinges on data quality, necessitating meticulous preprocessing steps such as cleansing, normalization, and feature engineering. Through techniques like data filtration and normalization, preprocessing refines data suitability for algorithms, enhancing predictive accuracy. In this study, we illustrate the transformative impact of preprocessing on predictive models' reliability. By contrasting accuracy tables for preprocessed and non-preprocessed datasets, we demonstrate the tangible benefits of preprocessing in refining predictive outcomes. The findings highlight the symbiotic relationship between machine learning algorithms and preprocessing techniques, emphasizing their crucial role in enhancing predictive capabilities in the dynamic real estate market landscape.",2.0,
9dcf895f71ae4f4b5b178a4c67ef78d1c8ca8d35,10.21834/e-bpj.v3i7.1233,House qualities characteristics relationship on house prices: Klang district,2018.0,,M. Masri; A. Nawawi; Edie Ezwan Mohd Safian; A. Saleh,The research was conducted to measure the characteristics quality of houses in Malaysia. The aim of this research was to produce significant results of the relationship between house qualities and house prices. The methodology used in this research was primarily by AHP and conducting Regression Analysis between the characteristics and house prices. Data collection gathered through a sampling of landed terrace houses in the Klang district was incorporated for this. The results from the research portray significant findings that can help improve quality of life for targeted households.,2.0,https://ebpj.e-iph.co.uk/index.php/EBProceedings/article/download/1233/pdf
a34939ebd1d20ba654297f426501ad47056b65a4,10.1007/s10640-025-00995-x,Adjusting to the New Normal: River Flood Risk and the Real Estate Market,2025.0,Environmental and Resource Economics,Henri Gruhl; Johannes Brehm; Arijit Ghosh; P. Breidenbach; Nils Aus Dem Moore,"Do individuals learn from salient river floods and update their risk perceptions accordingly? We exploit the Western European flood in 2021 to estimate changes in property values across German flood risk regions. Flood-prone areas in proximity to the flood or with higher climate change beliefs experience housing price declines, while we observe no statistically significant effects in other flood risk zones. Our results imply that flood risk discounts in the housing market will not adjust uniformly, which may result in suboptimal capital allocation in areas at risk of future flooding.",2.0,https://link.springer.com/content/pdf/10.1007/s10640-025-00995-x.pdf
91a8ac86818a7900f689711220dc331ec6bb8ef1,10.1145/3448734.3450808,Applying GSADF to Test Housing Bubble in China,2021.0,International Conference on Computing and Data Science,R. Tan; Tze-Haw Chan,"The purpose of this study is to detect the housing bubbles with date stamping in China ten major cities based on the monthly data from 2005M01 to 2017M10 and explore intrinsic linkages among them. With monthly housing price indexes-sourced from China Index Academy, this paper employs recursive regression GSADF test proposed by Phillips et al. to detect and date stamping bubbles, and correlation analysis and Granger causality test to explore the intrinsic linkages. Calculated via Eviews, the findings detect the existence of multiple bubbles in six periods. Correlation analysis and Granger Causality test further reveal the high consistency on housing price among cities and catalytic effect on housing price promoted by government policy, such as regional economic development policy. This study offers adequate instruments for price bubble detection and forecast, which will be used to set up an early warning system for policy makers. As for individual and institutional investors, the findings are benefit for them to understand China housing market and make portfolio investment.",2.0,
f0b7b5770ed252d0ad4fd1852ee58f35fe4fb88e,10.1109/conecct57959.2023.10234749,House Price Prediction System using Machine Learning Algorithms and Visualization,2023.0,"IEEE International Conference on Electronics, Computing and Communication Technologies",M. S. Supriya; G. Vinayak; Vinod Ramesh Patgar; V. Mahajan,"This paper introduces a real estate price prediction system that uses machine learning algorithms. The system is intended to forecast the price of a home grounded on its various features such as location, square footage, number of bedrooms and bathrooms, and other related factors. The system uses a variety of machine learning algorithms, including linear regression, decision tree regression, random forest regression, and artificial neural networks to make accurate predictions. Algorithms are trained on a large dataset of house prices and their associated characteristics to learn the relationships between these factors and the corresponding prices. The system is measured against various performance metrics, such as: B. root-mean-square and r-squared score, to measure its accuracy and effectiveness. The results show that the system can predict house prices with great accuracy, making it a valuable tool for real estate agents, home buyers and sellers.The implementation of this system could lead to better informed and more efficient decisions in the housing market.",2.0,
8bd8f36fe3f09fb3771c727514bb55dd49d767f9,10.1109/idicaiei58380.2023.10406621,Value Prediction of Real Estate Using Machine Learning,2023.0,2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0 (IDICAIEI),Ishan Danona; L. Rani; Durgesh Srivastava,"Real estate is property in the form of land and houses. Its prices depend on location, number of bedrooms, society, area, etc. India is second in the world regarding population growth, so affordable housing becomes very necessary. The real estate business is very vast. So, customers or sellers need to know the estimated price of the houses. This research covers that area and predicts housing prices. This work uses k-fold cross-validation and linear regression model. In K-fold cross-validation, the data is divided into k different subsets which are also called fold. A linear regression model is a machine learning model that describes the relations between a dependent variable and one or more independent variables. The data used in this work is for Bangalore state. The results obtained from this linear regression model are compared to real data. The results from this study prove that this model can accurately predict the prices of buildings and houses. Furthermore, this accurate prediction will help customers and sellers, and directly impact the economy.",2.0,
c11e705a12510f4918740f154d27fe1d0c10d98e,10.54691/bcpbm.v38i.4204,Boston House Price Prediction Based on Machine Leaning Methods,2023.0,BCP Business & Management,Ze Li,"A significant part of the U.S. economy is real estate and the housing industry. The level of housing prices influences the market's total price level to some extent. Data from Kaggle website is evaluated by using linear regression, random forest regressor and SVM regressor. There are 505 samples and the relationships between aim price with 13 different features. In terms of accuracy of the prediction, the method random forest is best method to predict house price. However, better model like XGboost could be chosen to improve prediction results.",2.0,https://bcpublication.org/index.php/BM/article/download/4204/4095
f4dcb2551d2077bc6c972a5dc4b241743fe294e2,10.1111/apel.12419,Land financialisation and housing prices: evidence from China,2024.0,Asian-Pacific Economic Literature,Yin-Xin Su; Xiao-Feng Yu; Mingzhi Hu; Yu-Zhe Wu,"The issue of land financialisation has received considerable critical attention, with the literature focusing on the definition and process of land financialisation and its contribution to urban development. This paper draws upon the capitalist spatial production theory to explore how the ongoing financialisation process in the land market is related to the housing market. After controlling for observed city characteristics and unobserved city and year fixed effects, we find a positive correlation between land financialisation and housing prices. Furthermore, our results suggest that this positive effect is strengthened in tier‐1 cities and when land transfer is fully marketised, but weakened when more affordable housing is available. Overall, this study contributes to the literature on land financialisation and housing prices by critically discussing the consequences of land financialisation on housing prices, providing new insights into the reasons for soaring housing prices, and enriching the knowledge of urban governance.",2.0,
db331c0f24629fe48021dad0bdeb8a42113ac2ce,10.1177/0739456x241253188,Spillover Effect of Urban Reconstruction from Announcement to Completion on Residential Prices: Application of Two-Stage Spatial Quantile Regression,2024.0,Journal of planning education and research,Chun-Chang Lee; Zhi Lin; Chih-Min Liang; Zheng Yu,"This study analyzed four urban reconstruction projects in Taipei City (Zhufeng Tianmu, Huaku Sky Garden, Leqin Sanxi, and Yongde Yanyan) that were completed in 2016 based on data released by the Urban Regeneration R&D Foundation. The housing transaction data spanned from January 2008 to December 2017. The difference-in-difference method was used in conjunction with quantile regression to analyze the spillover effects of urban reconstruction projects on neighboring areas during the various phases of these projects. The empirical results showed that, during the planning and announcement phase, the effect on housing prices was negative and statistically significant for mid-to-low-priced units but positive and statistically significant for high-priced units. During the construction and post-completion phases, the effect was negative and statistically significant for low-priced, mid-to-low-priced, and mid-priced units, with low-priced units being the most affected group.",2.0,
7ec177405264ea4fe9a1d925a7a0f33f4d1c6131,10.1109/aemcse55572.2022.00095,"Ensemble Learning Based Models for House Price Prediction, Case Study: Miami, U.S",2022.0,"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)",Xiuyuan Wu; Bohan Yang,"Predicting housing prices is beneficial for both investors and households especially for a large housing market like Miami. Although many scholars have already performed multifarious researches in various locations, no vigorous research on Miami’s housing market is conducted to our knowledge. The purpose of this research is to construct a satisfying predicting model to forecast Miami housing prices that provide reliable references for people in Miami. A number of machine learning methods and deep learning models are implemented in this paper, including SVR, Linear Regression, Random Forest, Neural Network, and XGBoost. Among all models, ensemble learning methods Random Forest and XGBoost produce the best results, the former of which achieved an adjusted R2 of 0.9234 and XGBoost obtained an adjusted R2 of 0.9254. Moreover, we also analyze the factors resulting in high housing prices in Miami with the acquired insights from these powerful ensemble learning models.",2.0,
8ea47f50b206f614f8714fb240bb0073eee242a5,10.1109/icaiss58487.2023.10250587,A Comparative Study of Regression algorithms on House Sales Price Prediction,2023.0,2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS),K. Kalaivani; C. Kanimozhiselvi; Z. H. Mohamed Bilal; G. Sukesh; S. Yokeswaran,"Accurate prediction of house sales prices plays a pivotal role in various real estate and financial applications. As the real estate market continues to grow and evolve, it becomes increasingly important to employ advanced machine learning techniques to model and forecast housing prices. This research study presents a comprehensive study that investigates the performance of various regression algorithms like Linear Regression, Random Forest, Gradient Boosting and Extra-Trees for house sales price prediction. By examining a wide range of regression techniques, this study intends to identify the most effective algorithm for accurately predicting the household prices. Experiments are conducted to assess the models using Kaggle house prediction dataset, which has 1460 instances and 81 features. Feature extraction is done to select the most informative features. The results reveal that Extra-Trees outperform other regression methods with top 30 informative features.",2.0,
8346464039bf4731ec2d12feb1b59cd8e813ac95,10.1145/3490700.3490705,Research on the Influence of Transportation Network Centrality on Housing Price Based on Big Data,2021.0,International Conference on Advancements in Computational Sciences,Feng Gao; Zhi-Bin Zhang,"Based on the big data of transportation network and housing price, geographic weighted regression is used to measure the degree of difference in the impact of urban road network and public transportation network centrality on housing prices. The results show that the degree of influence of the public transport network on housing prices is generally higher than that of the road network; the betweenness of the public transport network has the greatest impact on housing prices, followed by the direct centrality of the road network, the proximity centrality of the public transport network, the proximity centrality of the road network, and the public transport network. Network direct centrality, road network between centrality. Lanzhou's urban housing prices have strong traffic-oriented characteristics, and differentiated traffic improvement plans should be formulated according to the characteristics of the traffic network of different land parcels.",2.0,
d7bef759011b7bcd7012fa6f38d2d87b48efb3ff,10.1109/icirdc62824.2023.00154,Research on the Design and Application of House Price Prediction Algorithms and Model Based on Machine Learning,2023.0,"2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC)",Kexin Chen; Jianhui Huang,"With the development of the socio-economy, the real estate industry has grown rapidly worldwide. Housing prices, as an important indicator for measuring a city's economic development level, have significant impacts on people's lives, work, investments, and other aspects. Therefore, how to accurately predict housing prices has become a highly researched area of interest. In recent years, the continuous development and application of machine learning technology have provided new solutions for predicting housing prices. This article aims to study the data processing flow for housing price prediction. Through comparing and analyzing different algorithms, we will derive the optimal data processing flow and housing price prediction model, validate its prediction accuracy and stability, provide a reliable tool for the real estate industry, help make more scientific and precise decisions, and promote the continuous development of the industry.",2.0,
686bb0c38914d25c5205d0fa98c39b8a67d6f337,10.15517/rcs.v0i138.12090,SUBURBANIZACIÓN Y CAMBIO SOCIAL EN LA METRÓPOLI MADRILEÑA,2013.0,,Fernando Díaz Orueta; María Luisa Lourés Seoane,"The article focuses on Madrid and its recent suburban development. The suburbs occupy not only most of the Madrid Metropolitan Area, but also a significant part of the Madrid Region. We analyze the relation between suburbanism and new patterns of socio-spatial inequality. Segregation has been reinforced not only by housing prices, but also by long distances and mobility chances.",2.0,https://revistas.ucr.ac.cr/index.php/sociales/article/download/12090/11386
cf6cf0b367fdf26096325e0e2631a761b678f8eb,10.1145/3575882.3575939,Housing Price Prediction Using a Hybrid Genetic Algorithm with Extreme Gradient Boosting,2022.0,"International Conference on Computer, Control, Informatics and its Applications",M. Siregar; Pahlevi Wahyu Hardjita; Farhan Armawan Asdin; D. Wardani; Ardhi Wijayanto; Yessi Yunitasari; Muhammad Anshari,"Predicting property prices provides a better service for customers to evaluate and estimate price movement before their purchases. Some features including OverallQual and GrLivArea, which were selected when applying GA, become important features that can influence property prices. This research proposes a hybrid Genetic algorithm combined with the Extreme Gradient Boosting algorithm to predict real estate housing prices. The proposed scheme is evaluated by Root Mean Square Error, processing time, and the number of deleted features. The proposed scheme has been compared with the sole Extreme Gradient Boosting. The experimental results show that the proposed scheme produces the smallest root mean square error value of 0.129 compared to 0.133 of the sole Extreme Gradient Boosting. Furthermore, the predicted time of the proposed scheme is much better than the sole method.",2.0,
c55d457f096b1c1e66cba54097b2431a155ad6d3,10.1109/iccece65250.2025.10984723,Efficient Grey Wolf: High-Performance Optimization for Reduced Memory Usage and Accelerated Convergence,2025.0,2025 5th International Conference on Consumer Electronics and Computer Engineering (ICCECE),Lianghao Tan; Zhuo Peng; Xiaoyi Liu; Weixi Wu; Dong Liu; Ruobing Zhao; Huangqi Jiang,"This paper presents an efficient Grey Wolf Optimizer (EGWO) designed to address the limitations of the standard Grey Wolf Optimizer (GWO), focusing on reducing memory usage and accelerating convergence. The proposed method integrates Sinusoidal Mapping for enhanced population diversity and a Transverse Longitudinal Crossover strategy to balance global exploration and local exploitation. These innovations improve search efficiency and optimization precision while maintaining a lightweight computational footprint. Experimental evaluations on 16 benchmark functions demonstrate EGWO's superior performance in convergence speed, solution accuracy, and robustness. Its application to hyperparameter tuning of a Random Forest model for a housing price dataset confirms its practical utility, further supported by SHAP-based interpretability analysis.",2.0,
82f72df403257e27f51c19ed81908a1b468aa78a,10.1086/712323,Comment,2021.0,NBER macroeconomics annual,V. Ramey,"This insightful paper byGuren,McKay,Nakamura, and Steinsson (Guren et al.) contributes to the literature that seeks to translate effects estimated on data at one level of geographic aggregation to another level of geographic aggregation. It can also be seen as a companion paper to their paper on the effects of variations of housingwealth on consumption (Guren et al. forthcoming). The current paper develops a very clever method for recovering “partial equilibrium” effects from regressions using variation across subnational units, such as cities and states. It then applies thismethod to estimating the marginal propensity to consume out of housing wealth. Separately, it offers a solution to a puzzle that has arisen with respect to the widely used Saiz (2010) instrument used for house prices.",2.0,
17dc729925bee104f49fd611ac1f080f27094aab,10.1109/tocs56154.2022.10015915,Mass Appraisal of Real Estate Prices Using Improved BP Neural Network with Policy Evaluation,2022.0,"2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)",Yi Zheng; Bohan Yang; Ruichen Zhang; Zhi-Xin Bai; Yu Sun,"This paper proposes a new policy index neural network (NN), which uses the back propagation (BP) NN model optimized by genetic algorithm (GA) and particle swarm algorithm and the policy modeling consistency (PMC) index to quantify the relevant real estate policies. This study also trains and validates the big data of primary housing transactions of 33 properties in the main urban area of Weihai City, Shandong Province and evaluates the model performance using mean squared error, mean absolute percentage error, and R2 indexes. Subsequently, it compares the model performance under the GA and particle swarm algorithm optimization, respectively. The results show that the BPNN model with policies as independent variables is more accurate in predicting house prices in Weihai City.",2.0,
ae1c32cb82be60ec60de9e5f7316eedeb2d02431,10.1080/00036846.2019.1612030,Size and sign asymmetries in house price adjustments,2019.0,Applied Economics,Kurmaş Akdoğan,"ABSTRACT Long-run mean-reversion in real house prices is determined by the relative strength of fundamental factors against the short-run influences. This article suggests that the adjustment towards the long-run trend in house prices could display non-linear behaviour due to some intrinsic characteristics of the housing market. Accordingly, sign and size asymmetries as well as possible structural breaks are taken into account in a unit root testing exercise for twenty-nine countries. Our results suggest that mean-reversion exists for seventy percent of the countries in our sample. Moreover, the out-of-sample forecasting performance of our non-linear models in predicting house prices is better than a simple auto-regressive benchmark for some countries.",2.0,
e5367a390abd10b119333e3713260d389383bca3,10.53383/100388,Analyzing Pre- and Post-Pandemic Housing Market Trends in India: A Quasi- Experimental Approach Using ITS and Panel Data Analysis,2024.0,International Real Estate Review,Muskan Dugar; Trupti Dandekar Humnekar; V. Moovendhan,"The research delves into the influence of pre- and post-pandemic real estate housing price indices of cities in India. Using an interrupted time series analysis and panel data regression, this study evaluates the repercussions of the COVID-19 pandemic on residential housing in ten Indian cities over a span of thirteen years from 2010 to 2023. The results present a medley of outcomes on city-level trends, a prevailing postpandemic downturn observable across cities, and a subsequent positive shift as compared to pre-pandemic standards. The study brings to light the diverse impact and shock insulation exhibited by smaller cities, thus unveiling the variations among different cities.",2.0,
42e10e35e88aa6c59763f685d635aa3f934509a0,10.1016/j.frl.2020.101720,The range of uncertainty on the property market pricing: The case of the city of Shanghai,2020.0,,Jian Zhou; Yi-Xuan Shen; Athanasios A. Pantelous; Hui Zhang,"Abstract Property prices around the globe have seen very strong growth over the last two decades. Across various advanced countries, such a rapid and uncontrolled growth in house prices puts their economy in danger, and most importantly, their social integration and interconnectedness at great risk. In this paper, we develop a novel fuzzy linear regression framework using symmetric and asymmetric trapezoidal fuzzy numbers for determining the relationship of particular (non-) policy factors with the house prices. An interviewing questionnaire survey was conducted for collecting real data for the city of Shanghai to illustrate our theoretical treatment.",2.0,
53189da773fd97164d9ef2ff3aada3f80a3c3b86,10.1080/00128775.2018.1482769,House Prices and Current Account Evolution in the European Union: An Intertemporal Approach,2018.0,Eastern European Economics,I. Ban,"Based on the dynamic equation of the intertemporal current account model, we estimated a current account balance between 2000 and 2014 for the EU countries due to the role played by different determinants. More precisely, we presented the effect of fiscal balance and house prices along with other variables for the purpose of explaining the external disequilbria among the member states. Our results indicate that the last real estate bubble has worsened the external imbalances of the members within the European Union. We also identified an asymmetric effect of real estate prices upon the current account of various countries within the European Union.",2.0,
409193be85a6addfb8b9ea5cec8954a488a67c26,10.1111/1540-6229.12465,Down payment requirements and house prices: Quasi‐experiment evidence from Shanghai,2023.0,Real Estate Economics,Hao Shen; Hang Zhou; Zhengyi Zhou,"Using the regression discontinuity design, a quasi‐experiment approach, this paper establishes a causal relationship between the down payment requirement and house prices by exploiting a unique institutional background in Shanghai. In the unique setting, the required minimal down payment ratio jumps at the Inner Ring, a circular elevated highway, from 50% to 70% for a large group of buyers. With transaction level data from the largest real estate broker in Shanghai, we find that a lower required down payment ratio increases the apartment price by 138.8 thousand RMB, around 3.71% of the average transaction price.",2.0,
c37b3936fa038b50286214eff27342b60fb3168e,10.24191/myse.v7i2.10264,DETERMINING SIGNIFICANT FACTORS AFFECTING GREEN BUILDING PRICE USING MULTIPLE REGRESSION ANALYSIS,2020.0,,Nur Syafiqah Jamil; T. Mohd; S. Masrom,"The green building (GB) can be defined as the method of increasing the efficiency of the building and site. However, the spillover effect of GB price in Malaysia has not widely been discovered as there are limited case studies on this issue. This paper attempts to determine the significant factors that influence the GB price. The empirical experiment has been conducted to test the Multiple Regression Analysis (MRA) model on a real dataset of house prices in the area of Klang Valley, Selangor with GB specifications. The result showed that factors related to tenure have significant contributions to the GB price",2.0,
3dbd8cf65cd7a15c852c612df4bcfcda23e7f329,10.1109/ainit54228.2021.00082,House Price Prediction Based on a Machine Learning Model,2021.0,"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)",Dinglong Yu; Zhenxi Wang; Wei Wei,"Using the prediction on the dataset, the three authors modelled and predicted the Iowa house price dataset using support vector machines, random forests, and XGBoost, respectively, and finally compared the model accuracy using mean square error scores. Eighty percent of the data was used for model training, and the remaining twenty percent was used for model testing. In addition, the authors performed the necessary cleaning of the data and data. Finally, mixing hyperparameters to make the model more accurate is one of the main focuses of the discussion.",2.0,
4379213f1fc2bfce59d0edb44817356863a3a1f4,10.47363/jaicc/2024(3)357,House Price Prediction Using Machine Learning and Artificial Intelligence,2024.0,Journal of Artificial Intelligence & Cloud Computing,Fatbardha Maloku,"The escalating annual rise in housing prices introduces volatility and uncertainty into the real estate market, underscoring the critical need for accurate price forecasting systems. Predicting house prices accurately remains challenging due to the multitude of influencing factors. This study aims to identify and analyze key determinants affecting house prices, employing two established machine learning models. Through comparative analysis, the research will recommend the most effective model for enhancing the accuracy of house price predictions.",2.0,
f6e92a5904d5c9f8e402bd0c9db5cd10c4e894fe,10.1145/3508072.3508091,Developing a stable house price estimator using regression analysis,2021.0,International Conference on Future Networks and Distributed Systems,O. Aljohani,"Buying a house of their own is a goal of most people, regardless of where they come from and their purchasing power. In order to help people realize this goal within their means, regression techniques such as Multiple linear, Ridge and Lasso regressions and other methods may be applied to determine a fair price. To that end, this paper is based on analysis done by applying the three regression models on a dataset obtained from the King County, WA, United States, to understand which model is most effective to achieve the above goal.",2.0,
78ed6dbbf8de1bb4bf2af32a18a530f70953e878,10.1111/1467-8462.12346,The Effect of Foreign Demand on Residential Property Prices: Evidence from Australia,2019.0,Australian Economic Review,Chris Wokker; J. Swieringa,"This study estimates the impact of foreign demand for Australian residential real estate on property prices. Using postcode‐level administrative data and fixed‐effects regression techniques we find a positive relationship between foreign investment approvals and price growth. Our analysis suggests that, on average, foreign investment contributed between $80 and $122 to quarterly price growth in Sydney and Melbourne between July 2010 and March 2015. This is a small proportion of the increase in property prices over the period.",2.0,
9a9259d94766ecbba99acb6ad34c5c615bf58981,10.15396/eres2014_193,Exploring the determinants of residential property values in a crisis: evidence from Greece,2014.0,,Theodoros Mitrakos; C. Akantziliotou; Vassiliki Vlachostergiou; Sotiris Tsolacos,,2.0,https://eres.architexturez.net/system/files/pdf/eres2014_193.content.pdf
8951b93b5b56260948a7ae6c17527f47b6c797c4,10.1080/14445921.2014.11104384,Do Spatial Effects Drive House Prices Away from the Long-run Equilibrium?,2014.0,,Le Ma; Chunlu Liu,,2.0,https://dro.deakin.edu.au/articles/journal_contribution/Do_spatial_effects_drive_house_prices_away_from_the_long-run_equilibrium_/20919247/1/files/37195894.pdf
ce6c1fec05efea4edcf4beafa9e77bb229d6e22c,10.2139/ssrn.3470064,"Internal and External Determinants of Housing Price Booms in Hong Kong, China",2019.0,Social Science Research Network,Farhad Taghizadeh‐Hesary; Naoyuki Yoshino; Alvin Chiu,,2.0,https://doi.org/10.2139/ssrn.3470064
440205e73bb814f65e1e006a9582dc5c823e9e20,10.12677/hjdm.2017.72004,Analysis of Beijing Second-Hand House Price Based on Random Forest,2017.0,,Xiao-Tong Li,,2.0,https://pdf.hanspub.org/HJDM20170200000_61628239.pdf
0d5c85fb2b230c55c4e82a51c1fd2ea5adeac52c,10.1016/j.foar.2025.06.006,Unraveling nonlinear relationship of built environment on pre-sale and second-hand housing prices using multi-source big data and machine learning,2025.0,Frontiers of Architectural Research,Qian Zeng; Hao Wu; Lu-Yao Zhou; Xue Gao; Ning-Yuan Fei; B. Dewancker,,2.0,https://doi.org/10.1016/j.foar.2025.06.006
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,10.15760/hgjpa.2020.4.1.6,"Public Transportation Accessibility, Proximity to New York City, and Residential Property Values in New Jersey",2020.0,,Sang-Wan Lee,,2.0,https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1062&context=hgjpa
35974ce2320bc976b6e930c3a9175e75ce25b55e,10.47738/ijiis.v7i4.226,Enhancing Housing Price Prediction Accuracy Using Decision Tree Regression with Multivariate Real Estate Attributes,2024.0,IJIIS: International Journal of Informatics and Information Systems,Ahmar Dwi Utomo,,2.0,https://ijiis.org/index.php/IJIIS/article/download/226/151
b112b1ec8a3e99a2ef543b2c6c1eae57f6ec414f,10.2139/ssrn.1636633,Australian House Prices Affordability: An International Comparison of the Determinants of House Price’s Performance 1980-2009,2010.0,,K. Daly; Yiyang Liu,,2.0,https://doi.org/10.2139/ssrn.1636633
385044684fadff9c88fa5f3f6cf4597efc5ba4f7,10.1109/tocs53301.2021.9688839,Housing Price Mathematical Prediction Method through Big Data Analysis and Improved Linear Regression Model,2021.0,"2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)",Xinyu Yang; Ze-Sheng Yin; Jiayi Li,"Housing price prediction is a typical case of exploratory data analysis. This article takes housing prices in a certain city in the United States as an example, and uses a variety of linear models to predict and analyze the trend of housing prices. Based on Spark, we have implemented a distributed housing price prediction analysis model. Through the comparison of multiple linear models, we find out the best linear analysis model with the best prediction results.",2.0,
e12c7df1216b75ad39d2fe6c8fa156d04537b756,10.1063/5.0196051,Machine learning based approach for predicting house price in real estate,2024.0,7TH INTERNATIONAL CONFERENCE ON NANOSCIENCE AND NANOTECHNOLOGY,Mohammed Ali Shaik; P. Praveen; T. S. Kumar; M. Parveen; Swetha Mucha,,2.0,
47c116f45a4345b1c8ea851eea5f1e4de19c4dd0,10.2139/ssrn.3289359,Is It a Curse or a Blessing to Live Near Rich Neighbors? Spatial Analysis and Spillover Effects of House Prices in Beijing,2018.0,,K. Vergos; Zhi Hui,,2.0,
461baa2ed46843009e81369c0cc2cdaa5f0edf1c,10.1007/s10708-025-11284-x,"Return of house price premiums based on proximity to subway stations in the post-pandemic period in Seoul, South Korea",2025.0,GeoJournal,Dongwoo Hyun,,2.0,
6dfca4df5452e08685ae82c32091c522566b5b2d,10.6107/jkha.2019.30.2.035,A Research on Fluctuations of Housing Prices Using Text Mining,2019.0,Journal of the Korean housing association,Mong-Hyun Jang; Han-soo Kim,,2.0,
34a156ce3d3bde0a16bd869fc392677f973c3578,10.1007/978-3-642-35548-6_122,Spatial Analysis and Spatial House Price Index Construction: Evidence from Chengdu Housing Market,2014.0,,L. Xin; Si-Qi Zheng,,2.0,
517eae85083baf23eddf1a034b384644c9b50b82,10.1016/j.jrurstud.2026.104103,Urban amenity and settlement intention of rural migrants in China: The mediating role of housing prices,2026.0,Journal of Rural Studies,Zi-Ming Liu; Boxiao Zhang; Zhigang Wang,,2.0,
cdb76d714a8ac7bcf6615085a1fcaa242f2b0ccc,10.23843/as.17.3.1,Nonlinear Effects of Physical Characteristics of Apartment on Housing Prices: Focusing on Multivariate Adaptive Regression Spline,2018.0,,Jihun Oh; Kim Jeongseob,,2.0,
96278bbdc3c51ba6189674264b5b10d403159abe,10.1080/13504850701719900,"Determinants of interstate differentials in the cost of housing, 2006",2010.0,,Richard J. Cebula; Michael Toma,,2.0,
90e06b7bb6ecebf1bd06b390d994fc5f50d684a9,10.12677/sa.2020.93036,Analysis of the Correlation between Housing Price Data in Boston Based on the Regression Method,2020.0,Statistics and Application,Ran Zhao,,2.0,
8f782d8d97a57ebaaf57344a6d78709df63e185e,10.1061/(asce)up.1943-5444.0000757,"Expressway Proximity Effects on Property Prices in Hangzhou, China: Multidimensional Housing Submarket Approach",2022.0,Journal of urban planning and development,Hai-Zhen Wen; Zaiyuan Gui; Chuan-Hao Tian; Yan Song; Ganghua Zhou,,2.0,
b0023bd4fb0f3efa2e9a6c3cd1959ac8c9924b3f,10.22313/reik.2017.15.3.35,A Study on the Determinants of Housing Price Using Data Mining,2017.0,,Hae-jung Chun,,2.0,
bf9865681ac40358fef441eb2d7310c8fa652b99,10.25436/e2201t,Specifying multi-scale spatial heterogeneity in the rental housing market: The case of the Tokyo metropolitan area,2021.0,,Peng Zhan; R. Inoue,,2.0,
9c435075f7982b32fc7121442af81db890063d6c,10.1142/s2345748116500263,Which is Driver? Land Price or Housing Price: Examining the Urban Spatial Structure of Beijing,2016.0,,C. Ding; Yi Niu,,2.0,
2046cade490e25506aded107d8e6e3d5890c2a48,10.22313/reik.2019.17.2.37,A Study on Prediction of Housing Price Using Deep Learning,2019.0,Residential Environment Institute Of Korea,Hae Jung Chun; Hye-Seon Yang,,2.0,
73045d240986513a829692a61d88431e2b541bbd,10.2139/ssrn.3417527,Forecasting Australian Real House Price Index: A Comparison of Time Series and Machine Learning Methods,2019.0,Social Science Research Network,George Milunovich,,2.0,
a8bd68467b333bc91b55c4121279e53c5ab1ee8c,10.1063/5.0222464,Boston housing price prediction with different machine learning methods,2024.0,AIP Conference Proceedings,Zhangjie Xia,,2.0,
388a8de2981377977b1e070b9c73f6a7e843718f,10.2139/ssrn.4407202,Using Machine Learning to Estimate the Heterogeneous Impact of Airbnb on Housing Prices: Evidence from Corsica,2023.0,Social Science Research Network,D. Brunstein; G. Casamatta; S. Giannoni,,2.0,
7c18158dc06552fdff710eba792192dc4edc34dc,10.1080/00222216.1983.11969559,"Comments on an article, ""The relationship of local public park and recreation services to residential property values""; Use of multiple regression in recreation research: a discussion of several issues.",1983.0,,L. M. Arthur; T. Schroeder,,2.0,
62b1054f7f05499495778ca937d1b679ab7d46f8,10.1007/s12076-009-0030-z,Predicting housing prices at alternative locations and under alternative scenarios of the spatial job distribution,2009.0,,Liv Osland; I. Thorsen,,2.0,
6eb3df88f004d3b76d01d44c917cfde37a94f9b5,10.62206/sajm.31.1.2024.60-82,Factors Affecting House-prices: A Study of House-owners in Bhubaneswar Using the Hedonic Pricing Model,2024.0,South Asian Journal of Management,Abhiraj Malia; Biswajit Das; Aurodeep Kamal; B. Bisoyi; I. Satpathy,,2.0,
54892b44fa14ba39e0048010e0534ed7f970190e,10.1049/cp.2011.0288,Novel methods to demarcate urban house submarket - Cluster analysis with spatially varying relationships between house value and attributes,2011.0,,Danlin Yu; Jing-Yuan Yin; Fei-Yue Ye,,2.0,
407faad9ae20aecb0f0bfdc7f26059d6e1c01425,10.4028/www.scientific.net/amm.415.722,Influencing Factors Analysis of House Prices Based on Multiple Linear Regression,2013.0,,Cheng Wang,,2.0,
493a558fc60b6ed5772a095693bff9a02e93a35a,10.5821/dissertation-2117-96193,"Estudio de la estructura urbana e identificación y análisis del impacto de la localización de la actividad económica sobre las dinámicas territoriales : El caso de Bogotá, Colombia",2016.0,,Nancy Helena Ruiz Estupiñán,"El objetivo de la investigación es el estudio de los rasgos que caracterizan la estructura territorial de Bogotá y los 17 municipios de su área de influencia inmediata, y el análisis de los cambios ocurridos durante el transcurso de la última década. Así mismo, se busca mensurar la magnitud de los impactos que esta nueva estructura haya provocado sobre las dinámicas urbanas de los entornos en transformación. 
Se utilizó el método funcional del valor de interacción para la identificación de los sistemas urbanos locales; el método de umbrales de referencia para la delimitación del centro económico de la ciudad; métodos paramétricos para la identificación de candidatos y subcentros de empleo desde una perspectiva morfológica; indicadores de caracterización territorial para el estudio de los subcentros; y el análisis de regresión lineal para el estudio de la relación entre el cambio de uso y los valores inmobiliarios impulsados por las transformaciones sobre la estructura urbana. Se utilizaron principalmente las dos encuestas de movilidad aplicadas en la ciudad para los años 2005 y 2011, y las bases de datos del Catastro Distrital para los mismos periodos. 
Desde la perspectiva funcion",1.0,https://upcommons.upc.edu/bitstream/2117/96193/1/TNHRE1de1.pdf
fd14ae4936f587b2130427533de43d8771b32ccf,10.23977/etemss.2018.1629,Study on the Influence Mechanism of Monetary Policy on Real Estate Price Regulation,2018.0,,Hai-Long Wang,"Real estate prices in China have been rising since 1998, and in the short term, the phenomenon of price increasing fast is constantly appearing. Accordingly, our country has adopted a lot of macro-control policies to deal with the excessive rise in real estate prices. Monetary policy is an important means of national macro-control, which can play a role in regulating real estate prices. The study of the influence mechanism of monetary policy on real estate price regulation is of great significance to stabilize house price and promote the steady development of economy. 1. Reasons for the Rise in Real Estate Prices in Recent Years Since1998, with the withdrawal of welfare housing system, the continuous expansion of urban land resources, the price of urban real estate in China has been increasing year by year. The main reasons for the price increase are as follows: 1.1 Lack of welfare housing system Since the founding of the people's Republic of China, our country has been carrying out the welfare housing division system, that is, the residents' housing is transferred to the unit land by the state, and the units build their own residential houses and distribute them to the unit worker",1.0,http://www.clausiuspress.com/conferences/AETP/ETEMSS 2018/G3895.pdf
9b0f75821d083f6e02f7348fd14f32e87250553e,10.15760/etd.2911,"On Both Sides of the Tracks: Light Rail and Gentrification in Portland, Oregon",2016.0,,N. Rochester,.............................................................................................................................. i Dedication ......................................................................................................................... iii Acknowledgements .......................................................................................................... iv List of Tables .................................................................................................................. viii List of Figures ................................................................................................................... ix Chapter 1: Introduction ....................................................................................................1 Purpose of this Study ...................................................................................................... 7 Research Questions and Hypotheses ............................................................................ 10 Chapter 2: Literature Review .........................................................................................11 Defining Gentrification .........................,1.0,https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=3923&context=open_access_etds
73aef05853fe5bc9d29ad276670945adb4514a84,10.5821/dissertation-2117-95073,Sostenibilidad económica municipal de los crecimientos y servicios urbanos asociados al planeamiento urbanístico,2013.0,,G. Jiménez; F. Javier.,"El constante crecimiento de la urbanización de las ciudades hace necesaria una continua expansión de los servicios públicos municipales hacia las zonas recién urbanizadas. El coste del mantenimiento de las infraestructuras o servicios urbanos asociados está asociado a la distribución de los viales y espacios libres. Por ello la forma urbana afecta a la morfología de los elementos físicos del servicio, y por tanto incide sobre su balance económico de explotación. 
Dado que la estructura de la ciudad y particularmente la de sus espacios públicos es muy rígida en el tiempo, tiene gran trascendencia el adecuado conocimiento de las relaciones que vinculan la ordenación urbanística de un ámbito con los futuros ingresos y gastos generados en la gestión de los servicios públicos asociados. Con objeto de definir y cuantificar estos nexos de unión entre urbanismo y Haciendas Locales, se ha procedido a seleccionar de entre todas las competencias municipales aquellas relacionadas con el territorio. Globalmente se ha obtenido que, de media, el 41% de los ingresos y el 28% de los gastos corrientes del municipio se destinan a servicios íntimamente ligados a la ordenación urbanística. 
En un segun",1.0,
fa0f8599d21759a9074c1819c26916a5ca2d1ff6,10.5194/ica-abs-1-25-2019,Housing issue in shrinking Russian cities: mapping the reality,2019.0,Abstracts of the ICA,E. Batunova; M. Gunko,"Abstract. In Russia, housing issue has been remaining one of the most important political concerns since the beginning of the 20th century. In the recent political discourse, it is associated mainly with the need to provide new housing area and the improvement of housing conditions regardless of the difference in the development of regions or cities. However, the fact that over 70% of Russia’s cities shrink (Batunova and Gunko, 2018) requires developing specific approaches to housing policy’s formulation within depopulating territories. The phenomenon of urban shrinkage is generally understood as population decline which results in physical urban infrastructure surplus & degradation. Dilapidated or abandoned houses become the most evident sign of a city’s decline and one of the most important challenges for the local authorities. However, most examples known from the existing literature come from the countries of Western Europe and the US, where economic factors are the main drivers of urban depopulation. The real estate market reacts to the population decline by falling prices (Follain, 2010), which worsens the economic situation in the city and leads to a vicious circle of econom",1.0,https://www.abstr-int-cartogr-assoc.net/1/25/2019/ica-abs-1-25-2019.pdf
25eeb1891a59de6ff01ced50b8eabad125625969,10.24857/rgsa.v19n7-106,Land Use and Land Cover Changes in the Identity Territory Portal do Sertão from 1985 to 2022,2025.0,Revista de Gestão Social e Ambiental,Lázaro Pinheiro de Brito; Higo Batista Ferreira; R. N. de Vasconcelos; Deorgia Tayane Mendes de Souza; Luciana Gomes da Luz Silva; Washington de Jesus Sant'anna da Franca Rocha,"Introduction: Landscape changes can be monitored using geotechnologies through land use and land cover management over time, which helps identify and understand transformations in the landscape and supports decision-making processes. This study aims to analyze land use and cover change in the Portal do Sertão Identity Territory, Bahia, and to relate the possible causes that have currently induced such changes. This territory, located in the Caatinga biome, has its economic activities mainly concentrated in the primary and tertiary sectors. Remote Sensing (RS) and Geographic Information Systems (GIS) were employed to monitor changes in land use and cover in this territory from 1985 to 2022, using images from the MapBiomas Collection 8.0. During the study period, there was a reduction in the area used for agriculture and livestock and a significant increase in urbanization, with an urban area growth of more than 380%. Additionally, water bodies also expanded, mainly due to the construction of the Pedra do Cavalo Hydroelectric Plant in 1985, which resulted in a larger flooded area. Forest formations, although fluctuating over the years, were negatively impacted by agricultural expansi",1.0,https://rgsa.openaccesspublications.org/rgsa/article/download/12980/7372
d62b21c5a1bd0007db21d83d7bd877640304ae7b,10.1088/0026-1394/61/1a/08004,Subsequent bilateral key comparison study - organic solvent calibration solution - gravimetric preparation and value assignment of aflatoxin B1 (AfB1) in acetonitrile (ACN),2024.0,Metrologia,R. Josephs; M. Bedu; A. Daireaux; Xiu-Qin Li; Xiao-Min Li; Z. Guo; Xianjiang Li; T. Choteau; G. Martos; S. Westwood; R. Wielgosz; H. Li; H. Klich; R. Torkhani; W. Bellazreg; S. Chaieb; H. Sallemi; K. Alimi,"The original CCQM-K154.b comparison was coordinated by the Bureau International des Poids et Mesures (BIPM) and the Chinese National Institute of Metrology (NIM) on behalf of the Organic Analysis Working Group (OAWG) of the Comité Consultatif pour la Quantité de Matière (CCQM) for National Measurement Institutes (NMIs) and Designated Institutes (DIs) which provide measurement services in organic analysis under the 'Comité International des Poids et Mesures' Mutual Recognition Arrangement (CIPM MRA) and/or have participated in the BIPM's Mycotoxin Metrology Capacity Building and Knowledge Transfer (MMCBKT) project as part of its ""Metrology for Safe Food and Feed in Developing Economies"" Capacity Building Programme. Gravimetrically-prepared solutions having an assigned mass fraction of specified organic analytes are routinely used to calibrate measurement processes for the quantification of the same analytes in matrix samples. Appropriate assignments of the property value and associated uncertainty of calibration solutions thus underpin the traceability of routine analysis and are critical for accurate measurements. Evidence of successful participation in relevant international compa",1.0,
99a8bfd74474cdd02675ab6f0472b1e89fddc3de,10.1111/jtsa.12467,Recent Advances in Spatio‐Temporal Methodology,2019.0,Journal of Time Series Analysis,C. Wikle; S. Holan,"This special issue consists of a collection of articles that describe innovations in spatio-temporal methodology. Spatio-temporal statistics has been developing at a rapid pace over the past 25 years. The topic is briefly covered in Cressie’s (1993) comprehensive book on spatial statistics. Subsequently, Cressie and Wikle (2011) provided the first comprehensive book on spatio-temporal statistics, but it focused primarily on linear, univariate, and Gaussian methods. More recently, there have been numerous significant advancements in non-linear, multivariate, and non-Gaussian spatio-temporal methods, particularly those suited for ‘big’ data problems. Yet, each of these topic areas is relatively underdeveloped, and there are still many research challenges. Historically, spatio-temporal statistics methodology has developed more as an extension of spatial statistics rather than time series. That is, the emphasis has been on specification of models through their second-order structure, typically within a Gaussian process framework. Alternatively, dynamic spatio-temporal models (DSTMs) have been used to model processes for which it is more realistic to think of spatial processes evolving ",1.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jtsa.12467
da4629d53acc0b95cf8c75519377b4a0e4f5349d,10.22617/wps200066-2,Conditions for Effective Macroprudential Policy Interventions,2020.0,Social Science Research Network,F. Khan; Arief Ramayandi; Marcel Schröder,"This paper aims at identifying effective macroprudential policy (MPP) interventions and analysing the macroeconomic conditions that promote them. We define effective MPP interventions as those that stabilize its underlying target variable, such as credit growth, house price growth, etc. For our analysis, we construct a new database that documents the use of a large number of MPP instruments for 61 advanced and emerging market economies from 2000 to 2016. The new feature of the database is that it maps every recorded MPP intervention in these economies and over this period to stabilize a specific target variable category for banking, health, domestic loans, the exchange rate, foreign capital movements, and house prices. Using this dataset, we introduce a practical way for defining the macroprudential policy effectiveness. We find that MPP interventions are more likely to be effective when several prudential measures are taken together, but at the same time avoid the diminishing returns of repeated MPP tightening. Monetary tightening seems to override the effectiveness of MPP instruments. The output gap, credit cycle, external debt, current account, and global risk appetite also count fo",1.0,https://doi.org/10.22617/wps200066-2
59e6eb01eaba33ef87ac142b20948a0d62af002e,10.1108/jpif-03-2020-0031,Property investor decisions using income and rental ratio signals,2021.0,,B. Brotman,"PurposeThis paper, a case study, aims to consider whether the income ratio and rental ratio tracks the formation of residential housing price spikes and their collapse. The ratios are measuring the risk associated with house price stability. They may signal whether a real estate investor should consider purchasing real property, continue holding it or consider selling it. The Federal Reserve Bank of Dallas (Dallas Fed) calculates and publishes income ratios for Organization for Economic Cooperation and Development countries to measure “irrational exuberance,” which is a measure of housing price risk for a given country's housing market. The USA is a member of the organization. The income ratio idea is being repurposed to act as a buy/sell signal for real estate investors.Design/methodology/approachThe income ratio calculated by the Dallas Fed and this case study's ratio were date-stamped and graphed to determine whether the 2006–2008 housing “bubble and burst” could be visually detected. An ordinary least squares regression with the data transformed into logs and a regression with structural data breaks for the years 1990 through 2019 were modeled using the independent variables in",1.0,
3b4f3fc1877047dd5e83a91b72f72be0cf422abe,10.1088/0026-1394/60/1a/08023,Key Comparison Study - Organic Solvent Calibration Solution - Gravimetric preparation and value assignment of patulin (PAT) in acetonitrile (ACN) with 0.1 % formic acid (FA),2023.0,Metrologia,R. Josephs; M. Bedu; A. Daireaux; Xianjiang Li; Z. Guo; Xiu-Qin Li; Q. Zhang; Y. Gao; T. Choteau; G. Martos; S. Westwood; R. Wielgosz; Hong-Mei Li; M. Simón; C. S. Smersu; M. Villarreal; M. Rzeźnik; M. Cirio; E. C. P. Do Rego; R. Leal; L. Carvalho; E. Guimarães; L. M. Erazo; S. Ramírez; I. Gonzalez; I. Tahoun; P. Giannikopoulou; C. Alexopoulos; E. Kakoulides; M. Alrasheed; A. Alzahrani; S. Alsowailem; D. Prevoo-Franzsen; M. Fernandes-Whaley; S. Marbumrung; P. Jongmesuk; P. Kankaew; K. Shearman; C. Boonyakong; H. Klich; R. Torkhani; T. Gokcen; M. Bilsel; S. A. Ozen; O. Martínez,"The CCQM-K154.d comparison was coordinated by the the Bureau International des Poids et Mesures (BIPM) and the Chinese National Institute of Metrology (NIM) on behalf of the Organic Analysis Working Group (OAWG) of the Comité Consultatif pour la Quantité de Matière (CCQM) for National Measurement Institutes (NMIs) and Designated Institutes (DIs) which provide measurement services in organic analysis under the 'Comité International des Poids et Mesures' Mutual Recognition Arrangement (CIPM MRA) and/or have participated in the BIPM's Mycotoxin Metrology Capacity Building and Knowledge Transfer (MMCBKT) project as part of its ""Metrology for Safe Food and Feed in Developing Economies"" Capacity Building Programme. Gravimetrically-prepared solutions having an assigned mass fraction of specified organic analytes are routinely used to calibrate measurement processes for the quantification of the same analytes in matrix samples. Appropriate assignments of the property value and associated uncertainty of calibration solutions thus underpin the traceability of routine analysis and are critical for accurate measurements. Evidence of successful participation in relevant international comparison",1.0,
93f5f275e85401ef3ab8219c3b3a95c83e5e5b6b,10.61935/aedmr.1.1.2023.p62,A Study on the influence of China's green finance development on real estate prices,2023.0,Advances in Economic Development and Management Research,Xinze Chen,"At present, the importance of green finance in the world market is becoming increasingly prominent. Various kinds of green products have emerged, and auxiliary policies have also come into being. China is one of the world's largest carbon dioxide emissions and the world's largest investor in new energy. In the face of severe environmental problems and challenges of sustainable development, the Chinese government attaches great importance to the development of green finance, and has adopted a series of positive policy documents and guidance, including the establishment of special funds, providing preferential fiscal and tax policies, and the establishment of a green bond market to promote its role in the financial system. In addition, China has set up a set of green financial standards and evaluation system to guide financial institutions and enterprises to be more transparent and sustainable in environmental risk management and green investment. 
The emergence of green finance has brought a certain impact on the real estate industry, a pillar industry of China's economy. At present, many people in China, especially in some central cities, have speculated on housing prices. This beh",1.0,https://www.scien-publishing.com/index.php/index/details/download_url/51
e334c8e8bbfaf7089e1691d934b98df3c593b375,10.1088/0026-1394/60/1a/08002,Key comparison study - organic solvent calibration solution - gravimetric preparation and value assignment of deoxynivalenol (DON) in acetonitrile (ACN),2022.0,Metrologia,R. Josephs; M. Bedu; A. Daireaux; Z. Guo; Xianjiang Li; Y. Gao; Xiu-Qin Li; T. Choteau; G. Martos; S. Westwood; R. Wielgosz; Hongmei Li; M. Simón; C. S. Smersu; M. Villarreal; M. Rzeźnik; M. Cirio; E. C. P. Do Rego; R. Leal; L. Carvalho; E. Guimarães; A. Bahadoor; I. Rajotte; J. Bates; J. Melanson; L. M. Erazo; S. Ramírez; I. Gonzalez; D. Prevoo-Franzsen; M. Fernandes-Whaley; S. Marbumrung; P. Jongmesuk; P. Kankaew; K. Shearman; C. Boonyakong; T. Gokcen; M. Bilsel; S. Ozen; J. Cea; O. Martínez,"The CCQM-K154.c comparison was coordinated by the the Bureau International des Poids et Mesures (BIPM) and the Chinese National Institute of Metrology (NIM) on behalf of the Organic Analysis Working Group (OAWG) of the Comité Consultatif pour la Quantité de Matière (CCQM) for National Measurement Institutes (NMIs) and Designated Institutes (DIs) which provide measurement services in organic analysis under the 'Comité International des Poids et Mesures' Mutual Recognition Arrangement (CIPM MRA) and/or have participated in the BIPM's Mycotoxin Metrology Capacity Building and Knowledge Transfer (MMCBKT) project as part of its ""Metrology for Safe Food and Feed in Developing Economies"" Capacity Building Programme. Gravimetrically-prepared solutions having an assigned mass fraction of specified organic analytes are routinely used to calibrate measurement processes for the quantification of the same analytes in matrix samples. Appropriate assignments of the property value and associated uncertainty of calibration solutions thus underpin the traceability of routine analysis and are critical for accurate measurements. Evidence of successful participation in relevant international comparison",1.0,
54e194c39a605704a765d48fa07ee8e165527f0c,10.2991/icemeet-16.2017.5,An Empirical Study on the Influence of Price scissors of M1-M2 Growth on House Prices,2017.0,,Shuguo Zhang; Min He,"This paper analyzes the impact of China's money supply M1-M2 growth rate and interest rate on real estate sales price with VEC Model and Impulse Response Function. The study was based on 48-quarter time series data from 1999 to 2010 in China. The results show, the growth rates of M1 and M2 have a fast and significant impact on the real estate sales prices, in the second and fourth period to achieve positive significant. And there is a time lag in the impact of interest rates on house prices. Introduction At present, continuing to rise in the money supply, cutting deposit and lending interest rates and the sharply rising of housing sales price levels, link between monetary policy and housing sales price has become the focus of attention. Due to the uniqueness of the real estate industry, it highly connects with Banks and other financial institutions. From twentieth Century 70's to 90's the instance of real estate bubble caused by the improper monetary policy is not in the minority, such as Japan, America, southeast Asia, etc. In recent years, the rise of China's real estate prices is significantly accelerated and in some areas the increase is even more alarming. From the historical ",1.0,https://www.atlantis-press.com/article/25869065.pdf
260bf7c305416b70c402e3f71ea54a15cc68ae07,10.17533/udea.le.n79a6,Influencia de la migración en los precios de la vivienda en los municipios de Cundinamarca (Colombia),2013.0,,Hernán Enríquez,"Influencia de la migracion en los precios de la vivienda en los municipios de Cundinamarca (Colombia) Resumen:  El articulo evalua la relacion de interdependencia que existe entre el valor de la vivienda urbana y el crecimiento poblacional via migracion, bajo un modelo de mercado inmobiliario regional en Cundinamarca (Colombia). Se tiene en cuenta la accesibilidad a Bogota en la determinacion conjunta de estas variables, con el fin de determinar bajo que condiciones se ajusta el mercado controlando por factores espaciales. Se estima un sistema de ecuaciones para los precios de la vivienda urbana y la tasa neta de migracion para 116 municipios. El principal resultado indica que la mayor atraccion de poblacion de un municipio influye negativamente en el valor de la vivienda urbana. Como conclusion, se tiene que bajo la nocion de interdependencia precios-migracion mediada por el nivel de cercania a la capital, se configura un mercado inmobiliario regional que no se vincula entre municipios y que ofrece bajos niveles de amenidades a la poblacion en cada localizacion. Palabras Clave: migracion, mercado inmobiliario, amenidades. Clasificacion JEL:  C31, J61, R31. Influence of migration o",1.0,https://revistas.udea.edu.co/index.php/lecturasdeeconomia/article/download/17953/15410
3d7265ad4ad9bfaed1aa402411619ba8e0b3cea3,10.5821/ctv.8004,Concentración de inmigrantes y mercado inmobiliario en la ciudad de Barcelona,2014.0,"9° Congresso Città e Territorio Virtuale, Roma, 2, 3 e 4 ottobre 2013",Lenimar Nairt Arends Morales; M. P. Garcia-Almirall,"Diferentes autores mantienen que el sistema de precio de suelo y vivienda existente es un 
factor que perpetúa la segregación residencial y la desigualdad, situación que se agrava 
cuando se refiere a algunos grupos de inmigrantes que terminan alojándose en espacios 
degradados con características por debajo de las mínimas legales de habitabilidad (CORTÉS, 
2000; MUSTERD, 2003). 
Se parte de la hipótesis que la población inmigrante del municipio de Barcelona, se ubica 
principalmente en esas zonas de menor calidad de vivienda y, que estas zonas de 
concentración de inmigrantes y menor calidad coinciden con las viviendas de menor precio. Se 
construyó el Índice de Calidad (iQviv) y el Índice de Características (iCviv) de viviendas. 
Observando su relación con el Índice de Concentración de Inmigrantes y la evolución del valor 
de la vivienda 2004-2010 en Barcelona se constató que no siempre las zonas con menor índice 
de calidad de vivienda y donde se concentran los inmigrantes coincide con las que presentan 
los precios inmobiliarios más bajos. 
El mercado inmobiliario es un factor determinante de la estructura socio-espacial de la ciudad, 
a través del cual se puede modificar el mo",1.0,https://upcommons.upc.edu/bitstream/2099/16458/1/1333_1350%20MIGRAR%20INMIGRAR.pdf
411be354fe6760b0aaf9f811e21745a6a64dbaeb,10.32469/10355/6638,Three essays in regional economic modeling,2008.0,,Doleswar Bhandari,"My dissertation is about using regional economic modeling for economic impact analysis, forecasting, and for better understanding the local economy. In the first essay, I developed a nonspatial version of a community policy analysis model for Missouri counties. The model recognized the intersectoral linkages in the Missouri economy. The model consists of four modules: labor market, demography, housing market, and local public finance. Employment and total personal income drive the model. The model predicts reasonably well that increases in local employment lead to increases in local population, housing demand, local revenues, and demand for public services. However, by not considering the effects of space, the impact analysis and forecasting capabilities of my first model may not be as accurate as needed. Therefore, in my second essay, I introduce a spatial dimension into my model by specifying and estimating generalized spatial three-stage least squares procedures. The results show significant cross-county interactions within Missouri in terms of the supply of public goods, labor mobility, retail trade, and the choice of residential location. In my third essay, using South Korean ",1.0,https://doi.org/10.32469/10355/6638
e6f4c481aed9ea19397a2305dd6f117b346da348,10.59236/emro.v27i8a251,Smell of Money,2025.0,Educational Media Reviews Online,I. Stanishevskaya,"Distributed by Collective Eye Films, 1315 SE 20th Ave. #3, Portland OR 97214; 971-236-2056Produced by Shawn Bannon and Jamie BergerDirected by Shawn Bannon2022, Streaming, 85 mins
This documentary critically examines the environmental justice challenges confronting low-income populations, primarily communities of color, residing in proximity to industrial hog operations in Eastern North Carolina. Although pork production serves a pivotal function in sustaining the food industry and addressing global nutritional needs, the film underscores the adverse externalities associated with intensive hog farming, including substantial ecological degradation and harmful impacts on human health and quality of life.
Through compelling interviews and firsthand accounts, the documentary illustrates how massive amounts of animal waste from these operations contaminate the soil, air, and water, leading to numerous health issues, including respiratory illnesses, skin and eye irritations, and mental health challenges. Individuals living near these operations are not merely passive observers of pollution; they often represent an at-risk population directly affected by an industry that frequently operat",1.0,https://doi.org/10.59236/emro.v27i8a251
2f79fbcd8496f0e43981a843bb173b11a1713669,10.54861/27131211_2024_5_195,APPLICATION OF ECONOMETRIC METHODS OF REAL ESTATE MARKET ANALYSIS ON THE EXAMPLE OF KOSTROMA,2024.0,Прогрессивная экономика,С. М. Глухова; А. Ю. Чернов,"Целью статьи является проведение комплексного анализа факторов, влияющих на стоимость жилья в городе Костроме, с использованием методов корреляционно-регрессионного и факторного анализа. В статье используются методы корреляционно-регрессионного анализа для моделирования влияния различных факторов на стоимость жилья; на основе факторного анализа исследуются количественные показатели (например, площадь, этажность, расстояние до центра города) и качественные переменные (например, тип теплоснабжения). В соответствии с результатами моделирования стоимости жилья было выявлено, что на цену жилья влияют такие факторы, как расстояние до центра города, этаж, количество комнат, тип теплоснабжения и материал стен. Данные факторы будут продолжать оказывать влияние на стоимость жилья, следовательно, можно прогнозировать дальнейший рост цен на квадратные метры в центральных районах города. Исследование тенденций развития рынка недвижимости показало, что после относительной стабильности до 2020 года наблюдается большой рост цен на недвижимость, что затрагивает первичный, вторичный рынки и рынок аренды. Цены на вторичном рынке жилья обогнали цены на первичном рынке, что приводит к повышенному спрос",1.0,https://doi.org/10.54861/27131211_2024_5_195
dc9d287b9443746d85f13a05a9f9a46bf9934de3,10.29313/bcss.v3i2.9084,Penerapan Metode Regresi Komponen Utama Kernel untuk Prediksi Harga Rumah,2023.0,Bandung Conference Series: Statistics,M. Verdiansyah; Suwanda,"Abstract. Linear regression analysis is a statistical method used to model the relationship between the dependent variable and one or more independent variables expressed in the form of a regression equation. The Ordinary Least Square (OLS) can be used to estimate regression parameters. The regression parameter estimator obtained will be good if the assumptions that apply to MKT are met. One of the assumptions that must be met is that there is a linear relationship between the dependent variable and each independent variable. If linearity is violated, kernels can be used. In addition, another assumption that must be met is the absence of multicollinearity between independent variables. An alternative method that can be used to overcome multicollinearity is using Principal Component Analysis. If the linearity and multicollinearity assumptions are violated, the Kernel Principal Component Regression method can be applied. This study will apply the Kernel Principal Component Regression (KPCR) method, where the principal component is a nonlinear combination of independent variables through a kernel function. This KPCR method is implemented to predict house prices in South Jakarta based ",1.0,https://proceedings.unisba.ac.id/index.php/BCSS/article/download/9084/3528
6a012daba742bf04502b35ae5ff4cb773ec6e865,10.1108/ijhma-10-2019-0104,Green federal tax credits impact on US housing prices,2020.0,,B. Brotman,"The purpose of this study is to investigate whether increases in homeowner green amenities occurred because of income tax credits to the degree that changes in housing prices are measurable. Are higher incomes, lower mortgage rates and green income-tax credits impacting housing price changes?,The paper uses the least-squares regression model with natural log specifications. The log of income and a dummy variable, which was assigned to the Energy Policy Act (2005) and the American Recovery and Reinvestment Act (2009) coverage dates are used as independent variables. Two regression models were examined using monthly housing price data from January 1990 through the year 2018. The first regression model used a single dummy variable for credits available under the Policy Act of 2005 and the Recovery Act of 2009. The second regression model considered the credits granted under these two laws separately. Disposable income per capita impacts demands for housing while green upgrade expenditures affect the cost of housing.,The laws set low credit limits of $500 followed by $1,500 but because of the multiplier effect, the spending appears to have magnified and been much higher. The credit ava",1.0,
cd0253cfb462617f4645af2f8995b6e986e4ecc0,10.1080/10511482.2018.1506389,Realizing the Potential for Increased Educational Attainment to Support Higher Homeownership Rates,2019.0,Housing Policy Debate,Christopher E. Herbert,"With the precipitous—and unprecedented—decline in the homeownership rate from its peak of 69.0% in 2004 to a low of 63.4% in 2016 there has been no shortage of analyses to assess the likely future path of this important housing market metric (U.S. Census Bureau, 2018). Most of these studies employ a methodology that combines population projections by age and race/ethnicity with assumptions about the potential for recent trends in household formation and homeownership to continue or moderate (Acolin, Goodman, & Wachter, 2016; Myers & Lee, 2016; Spader & Herbert, 2017), reaching a general consensus that rates are likely to be lower in the future, although the range of declines projected is quite broad. Myers and his colleagues (2019) make an important contribution to this literature by examining how changes in the characteristics of households beyond their age and race/ethnicity might affect these trajectories. Their methodology also provides an opportunity to assess the relative contribution of shifts in the characteristics of borrowers versus changes in the market context. In both of these areas the article comes to significant conclusions. First, the authors find that plausible ch",1.0,
7414dba8d219b9bd23af2cafcb3486a4bff78f3f,10.1145/3473714.3473845,The Evaluation of the Health of Chinese Real Estate Market: Empirical Research based on index clustering and AHP,2021.0,International Conference on Control and Intelligent Robotics,Jing Hou,"Since the reform of the housing system in 1998, China's housing industry has developed vigorously, and the real estate market's output and prices have rocketed, hence becoming a key engine for economic growth. Compared with developed countries such as the United Kingdom and the United States, the Chinese real estate market has a much shorter history of development. In the infancy stage of Chinese real estate market, there existed various problems in the market, i.e., the continuous rise in housing prices, housing bubbles, land finance, ""ghost cities"", etc, which have seriously affected the long-term stable development of Chinese real estate market. In 2003, the ""Circular of the State Council on Promoting the Continuous and Healthy Development of the Real Estate Markets"" first proposed that ""The significance of the continuous and healthy development of the real estate markets shall be fully understood."" Since then, China has put forward the concept ""health of the real estate market"", and the government has frequently issued various measures for macro-control, but they have little effect. One of the principle reasons for the ineffectiveness of multiple housing macro-control policies ",1.0,
2e593eab46c48f98f1b7c1470f355911f9333927,10.15396/afres2014_121,GOVERNMENT POLICIES AND HOUSEHOLD MOBILITY BEHAVIOUR IN NIGERIA: THE CASE OF OSUN STATE PROPERTY DEVELOPMENT CORPORATION (OSPDC),2014.0,,O. Adegoke; Ikpeme Ankeli; D. Dabara; I. Akinjogbin,"Purpose – This paper examines government housing policies and how it affects household mobility behaviour in Osun State Nigeria. Design/methodology/approach – Primary data used in the study was obtained through oral interview of randomly selected officials of the OSPDC and purposively selected households’ situated in OSPDC estates in the six geo-political zones of the state. Similarly, secondary data was collected from the official records of the corporation. Simple descriptive statistical tools such as frequency distribution, mean and percentages were employed to analyze the data collected. Findings – The study revealed that the major determinants of household mobility behaviour in the state are accessibility and affordability of housing units, other factors include: household type, size class of households, type of dwelling/housing units, number of exclusive sleeping rooms and tenure status. Also, the study revealed that in Nigeria, particularly Osun state, the government through the agency of the OSPDC encourages household mobility through the provision of accessible and affordable housing to particularly the low and middle-income classes. However, much is still left to be desir",1.0,https://afres.architexturez.net/system/files/afres2014-121.pdf
50f372d620332a2f9e17c0ffd5215d6a54642b09,10.67330/kgebxf46,Residual Constraint Approach (RCA): Framework & Protocol,2026.0,Valuation Engineer Journal,W. Craytor,"A significant limitation of conventional appraisal methods is their reliance on outdated manual techniques, such as matched pair analyses, which bypass property feature value contributions as if they didn't exist: the traditional sales grid has no column for value contributions, only adjustments. In fact, value contributions provide the necessary basis for mathematical constraints that prevent over and undervaluation by traditional appraisers.
 The Residual Constraint Approach (RCA) advances the traditional Sales Comparison Approach (SCA) by integrating a multi-phase valuation process that employs Multivariate Adaptive Regression Splines (MARS) alongside rigorous mathematical constraints on feature value contributions, in particular unmeasured features — latent variables such as aesthetics, design, condition and quality — whose values are typically subject to subjective judgment and bias by the traditional appraiser.
 The mathematical constraints in RCA rely on the expert application of MARS regression to estimate the value contributions of measurable property features to the sale price. However, this estimate typically accounts for only about 80% of the actual sale price, excludin",1.0,https://journal.valuation-engineer.com/index.php/vej/article/download/9/109
d8cad0ce827b80811da6934abb2a21976e0bf037,10.1108/ijhma-01-2025-0003,"Socio-spatial exclusion in housing: unpacking market trends in Hawassa City, Ethiopia",2025.0,International Journal of Housing Markets and Analysis,Tesfaye Gebeyehu Admasu; J. Lupala; F. Magina,"Purpose
Increasing number of residents in rapidly urbanizing cities of sub-Saharan African are experiencing growing exclusion. This study aims to examine socio-spatial exclusion in housing markets and seeks to understand policy and market-based trajectories of exclusion in Hawassa City, Ethiopia.

Design/methodology/approach
A mixed-methods approach was used, combining primary and secondary data. Interviews, document reviews and housing price data were gathered. Tender-based data for 1,927 plots over a 10-year period was obtained from the city’s land development department. In addition, 120 Global Positioning System-based housing price data points were randomly collected, considering location in X-Y coordinates, housing types, distance from the central business district (CBD) and sale repetition. Data were analyzed thematically, and spatial patterns of housing prices were mapped using techniques like inverse distance weighted interpolation and Moran’s clustering in ArcGIS.

Findings
The study revealed a growing trend in housing prices driven by policy and market forces, leading to spatial exclusion. Gaps in housing policy drove demand for peri-urban areas, fueling black markets and",1.0,
db5246785befa0babf1ebcb480ca61136333ec37,10.25158/l7.2.6,"Art as Protest, Cooking as Resistance",2018.0,Lateral,Lily Wei,"From 2010 to 2013, during the height of Taiwan’s housing rights movement, Participatory Art became instrumental in defending the right to the city. In this housing rights movement, artists, students, residents, and other professionals united to challenge neoliberal urban development. Two protest art projects in Taipei, Operation Little Barbarossa and Cooking at the Front Line, illustrate the interdisciplinary, trans-social strata collaboration. The artworks responded to encroachment on land and estate by the Taipei City government and real estate developers. The art forms employed included performance art, dance, writing, sculpture, graf ti, graphic design, and photography. Through everyday acts, such as cooking and driving, the two works lent voice and visibility to marginalized residents. The language and imagery of these protest gestures produced a theatricality that was at once jovial, amiable, critical, and contentious. The coexisting confrontational and convivial tones also encapsulate Taipei’s housing rights movement, in which the Taiwan Alliance for Victims of Urban Renewal exercised a central role. This article integrates ndings from archival analysis, interviews, particip",1.0,https://doi.org/10.25158/l7.2.6
c137f0b0cbd9f1b9162a40f83abec7b65e44a38d,10.29038/2786-4618-2025-01-71-80,ФІНАНСОВІ ІНСТРУМЕНТИ РОЗВИТКУ РИНКУ ЖИТЛОВОЇ НЕРУХОМОСТІ КНР: УРОКИ ДЛЯ УКРАЇНИ,2025.0,Economic journal of Lesya Ukrainka Volyn National University,Олена Володимирівна Борисюк; Олексій Мельник,"Introduction. The residential real estate market at the beginning of the 21st century. became the driving force behind the growth of the PRC economy, giving a powerful impetus to the development of related types of economic activity. The article presents the characteristics of its features, including mechanisms for supporting real estate investments, identifies problems and prospects for the functioning of the housing market, taking into account the risks of a repeat of the mortgage and financial crises according to the US scenario; defines the role of the state in regulating the residential real estate market of the PRC. The results of the study can be useful in assessing the situation on the residential real estate market and in the construction industry of Ukraine. 
The purpose of the article. The purpose of this study is to determine the financial instruments of the development of the residential real estate market of China and adapt foreign experience to domestic realities. 
Methods. In conducting the study, general scientific and special methods were used, in particular: retrospective analysis - to study the features of state support for the residential real estate market of ",1.0,https://echas.vnu.edu.ua/index.php/echas/article/download/977/801
8272aafa0a4fa1eb4afb53f1adde88695fa8e133,10.2105/ajph.2012.301098,"Food price, obesity and climate change",2012.0,American Journal of Public Health,Adam Drewnowski; Anju Aggarwal; P. Hurvitz; P. Monsivais; C. Rehm; A. V. Moudon,"Food prices are indeed a mechanism that links obesity and poverty.1 As incomes decrease, energy-dense grains, sweets, and fats become the best way to provide daily calories at a manageable cost.2 Added sugars and fats can be inexpensive, flavorful, satisfying, readily accessible, and convenient,3 but these ingredients can also provide minimal nutritional value. One factor behind rising obesity rates may be lower diet cost.4 
 
In the United States, the most obese neighborhoods are those with low residential property values, few amenities, and high poverty rates.5 Richer and better educated people live in wealthier areas, shop at upscale supermarkets, enjoy high quality diets, and are more likely to be thin and healthy.6 Less food-secure people with lower incomes shop at lower-cost supermarkets, search for bargains, and may be driven by economic necessity to select lower-quality diets. Obesity in America is largely a socioeconomic issue.7 
 
A rise in food prices caused by climate change will lead to higher, not lower, obesity rates in the United States. The spikes in food prices observed in 2008 and again in 2010 were highest for the healthier foods, particularly vegetables and fru",1.0,https://www.ncbi.nlm.nih.gov/pmc/articles/3518359
880bd6cddb821c525f1a2faee4bef1fa57f6fbab,10.1007/s42452-025-06922-5,Optimizing multivariate adaptive regression splines (MARS) with coordinate descent to accurately select the best model for house price prediction,2025.0,Discover Applied Sciences,Ndagijimana Adonia; Waweru Mwangi; Kimwele Michael,"Multivariate Adaptive Regression Splines (MARS) have proved to be a powerful tool for modeling non-parametric regression analysis problems and for selecting the model in multi-dimensional data. A major challenge lies in model over-fitting and under-fitting on multi-dimensional data. Applying optimization methods to design and select the model can reduce the over-fitting and under-fitting in MARS modeling. Particularly, information-based model selection criteria have shown to be effective for modeling in MARS. This study proposes a model selection method in MARS using coordinate descent (CD-MARS) that can accurately select the best model. We have integrated coordinate descent (CD) features in the information-based model selection and evaluation criteria formulation. In this formulation, the CD was used as a penalty term to the negative log-likelihood in information-based model selection and evaluation criteria. To test the model, the study generated a dataset for testing the model and then the study applied house prices dataset that is publicly available online to evaluate the model’s success. The dataset have the variables that do and do not add to the dependent variable. We measur",1.0,https://link.springer.com/content/pdf/10.1007/s42452-025-06922-5.pdf
4286323a5fd86880194b1e200225f51d7b424955,10.3389/fpubh.2024.1387850,"A longevity level-oriented wellness target area identification method: a case study of Yunnan Province, China",2024.0,Frontiers in Public Health,Yu Wang; Jia-Xue Wang; Xiao Wang,"Background Aging, as a global demographic issue, is characterized by its rapid growth, which drives an increase in people's healthcare awareness. The emergence of wellness bases caters to this market demand. Therefore, the identification of potential areas suitable for wellness activities and the construction of wellness bases, referred to as Wellness Target Areas (WTAs), becomes a crucial first step. Currently, commonly used identification methods are mostly based on traditional statistical approaches, which are often complex, cumbersome, and subject to potential risks of subjective assumptions, affecting the reliability of WTAs identification results. Longevity level serves as a comprehensive indicator reflecting the natural and socio-economic environment of a region, making it the most indicative of the regional wellness environment status. Methods This study proposes using longevity level as the benchmark for WTAs identification to simplify the identification process and reduce the impact of subjective bias on the results. The study focuses on 129 county-level units in Yunnan Province. Firstly, the Geodetector (GD) is utilized to explore the complex interaction between the long",1.0,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11199866
2d677de3cfd045c91d321d7614bfe7d7374a7677,10.52968/1506,"The Social and Economic Implications of Gentrification in Somolu LGA., Lagos State",2025.0,African Journal of Housing and Sustainable Development,V. Onifade; O. Michael; S. Adeyemi,"Gentrification in Somolu, Lagos, has significantly transformed the urban environment, reshaping housing affordability, demographic composition, economic activities and social interactions. This study examines the socioeconomic implications of gentrification in areas such as Bajulaiye, Pedro, Akoka, and Bariga, where noticeable changes in residential and commercial landscapes have emerged. The study employed the multistage sampling technique. The population was divided into two clusters of medium- and highdensity areas, and then subdivided into different strata consisting of stakeholders relevant to the study. Using Yamane’s formula, the researchers sampled 411 respondents. Focus group discussions were also done as a means to enrich the qualitative data. Using factor analysis, regression analysis and correlation analysis, the researchers identified key drivers of gentrification, such as rising rental rates, shifts in ethnic composition, demographic changes, and increasing property values. The findings reveal that while gentrification has led to improved infrastructure, increased business activities and enhanced public services, it has also resulted in significant displacement of lon",1.0,
9f2804394335ac6c50ab2545d0d7542c9889095d,10.1038/s41598-026-45218-5,Park green space accessibility and equity for age-friendly urban living in Kunming’s central district,2026.0,Scientific Reports,Jia-Xin Song; Jian-Song Peng,"With the accelerated aging process in China, the demand of the elderly for urban park green spaces has surged, making spatial accessibility and equity critical topics in the development of age-friendly cities. This study employs a community-scale spatial analysis approach, using Kunming’s main urban area as a case study, to develop a multidimensional assessment model. This model systematically evaluates the service efficiency of urban public green spaces for the elderly and proposes targeted spatial optimization strategies. By applying the Gaussian-based Two-Step Floating Catchment Area Method (Ga2SFCA) and integrating multimodal travel data (walking, cycling, public transit etc.), this study quantitatively reveals the spatial differentiation of park accessibility for the elderly. Results indicate that under both single-modal and multimodal travel scenarios, central urban areas exhibit significantly lower accessibility, with communities of extremely low accessibility accounting for 15.79%-52.15% of the total. Furthermore, a strong spatial correlation is identified between the total green space area in communities and the Comprehensive Aging Index (CAI), highlighting the need for in",1.0,https://www.nature.com/articles/s41598-026-45218-5_reference.pdf
a44e7aed3423e176960d0a271bac4b7f3e9345ea,10.1108/pm-02-2021-0018,"Bankruptcy filings, flooding, real estate prices and Leading Index",2021.0,Property Management,B. Brotman,"PurposeFlood damage to uninsured single-family homes shifts the entire burden of costly repairs onto the homeowner. Homeowners in the United States and in much of Europe can purchase flood insurance. The Netherlands and Asian countries generally do not offer flood insurance protection to homeowners. Uninsured households incur the entire cost of repairing/replacing properties damaged due to flooding. Homeowners’ policies do not cover damage caused by flooding. The paper examines the link between personal bankruptcy and the severity of flooding events, property prices and financial condition levels.Design/methodology/approachA fully modified ordinary least squares (FMOLS) regression model is developed which uses personal bankruptcy filings as its dependent variable during the years 2000 through 2018. This time-series model considers the association between personal bankruptcy court filings and costly, widespread flooding events. Independent variables were selected that potentially act as mitigating factors reducing bankruptcy filings.FindingsThe FMOLS regression results found a significant, positive association between flooding events and the total number of personal bankruptcy filin",1.0,
ab421f36054740378ab9eea35d4975b032ed3786,10.33042/2522-1809-2025-1-189-280-284,STATE OF THE REAL ESTATE MARKET IN CURRENT CONDITIONS: ANALYSIS OF TRANSFORMATION PROCESSES,2025.0,Municipal economy of cities,K. Mamonov; V. Goi; R. Viatkin,"It is proven that real estate is one of the important assets that affects the functioning and development of regions, the effectiveness of stakeholder interaction, and the possibilities of land use. Of particular importance is a reliable and complete assessment of real estate, which allows forming a quantitative basis for making informed decisions.
The goal has been achieved in terms of determining the state and transformation processes taking place in the real estate market in modern emergency conditions. The tasks solved: substantiation of theoretical provisions on the definition of real estate; determination of areas of real estate market analysis; characterization of ways of real estate valuation, the results of which are used for analyzing the real estate market; establishment of features of regulation of valuation activities; determination of practical aspects of real estate market analysis.
A definition of real estate is proposed as an asset that includes land plots, residential and non-residential objects, which is characterized by technical, functional, spatial, urban planning, and environmental parameters around which the underground and above-ground environment is formed",1.0,https://khg.kname.edu.ua/index.php/khg/article/download/6477/6397
329bb3dc33477735c2ff16b854fd6b136c64c641,10.1145/3240323.3241728,Adapting session based recommendation for features through transfer learning,2018.0,ACM Conference on Recommender Systems,Even Oldridge,"This industry talk covers the deep learning architecture developed at Realtor.com to recommend real estate listings to our userbase. The recommendation of homes is a different problem than most other domains both in the sense that listings are unique and that there are additional geographic and time constraints that increase the sparsity of interactions and make recommendation of individual listings more challenging. In particular time on market in a hot area can be limited to weeks or even days, and listing cold-start is critical to providing up to date market information. Thankfully the structured feature data for listings is incredibly rich and provides a framework from which to map listings into a meaningful vector space. User first impressions are also incredibly important in this highly competitive field, and offline recommendation or models that don't adapt during the users session are less desirable. In order to solve this recommendation problem we have developed a model based off of session based recommendation [1]. The architecture utilizes state of the art techniques from Natural Language Processing, including the AWD-LSTM language model developed by Salesforce [2]. To s",1.0,
f8e1e2babfc7f3b3cfb1398cf6ccf32ee9555da5,10.3390/su18021059,Spatiotemporal Evolution and Maintenance Mechanisms of Urban Vitality in Mountainous Cities Using Multiscale Geographically and Temporally Weighted Regression,2026.0,Sustainability,Man Shu; Honggang Tang; Si-Cheng Wang,"Investigating the characteristics and influencing mechanisms of urban vitality in mountainous cities can contribute to enhanced urban resilience, optimised resource allocation, and sustainable development. However, most existing studies have focused on static analyses at single spatial scales, making it difficult to fully reveal the evolutionary trends of urban vitality under complex topographic constraints or the spatiotemporal heterogeneity of its influencing factors. This study examines Guiyang, one of China’s fastest-growing cities, focusing on both its economic development and population growth. Based on social media data and geospatial big data from 2019 to 2024, the spatiotemporal permutation scan statistics (STPSS) model was employed to identify spatiotemporal areas of interest (ST-AOIs) and to analyse the spatial distribution and day-night dynamics of urban vitality across different phases. Furthermore, by incorporating transportation and topographic factors characteristic of mountainous cities, the multiscale geographically and temporally weighted regression (MGTWR) model was applied to reveal the driving mechanisms of urban vitality. The main findings are as follows: (1)",1.0,https://doi.org/10.3390/su18021059
e32d9467d2bf0eeab61b844a88273e31ecf072ce,10.4236/me.2021.122017,Can Purchase Restrictions Really Limit House Prices,2021.0,Modern Economy,Y. Shao,"This article uses the data of residential commercial 
housing prices in 70 large and medium-sized cities from 2007 to 2017, and on 
the basis of controlling the covariates that can affect housing prices on the 
demand side and the supply side. The method of double difference is used to 
study the two rounds of purchase restriction policies, that is, in 2010, the first round of purchase restriction policies 
promulgated by local governments from 20111 and the second round of purchase 
restrictions promulgated in 2016 have their respective effects on residential 
commercial housing prices and price growth rates. The regression results of the 
benchmark fixed-effects model show that the implementation of the first round 
of purchase restriction policies in cities can significantly reduce the price 
of residential commercial houses, and the long-term effects of the purchase 
restriction policies are relatively weak. Because there may be a certain 
reverse causality between the implementation of the purchase restriction policy 
and the city’s housing prices, this paper introduces the lag value of the 
explained variable as the instrumental variable of the purchase restriction 
policy, a",1.0,https://doi.org/10.4236/me.2021.122017
2500435517851839a440c3ed1b622c7026d31ea0,10.1145/3630106.3658965,Impact Charts: A Tool for Identifying Systematic Bias in Social Systems and Data,2024.0,"Conference on Fairness, Accountability and Transparency",Darren Erik Vengroff,"We introduce impact charts and apply them to problems of systematic bias encoded in three different data sets. Impact charts are highly visual, making the effects they find easy to understand by both domain experts and non-experts. Impact charts are nonlinear and non-parametric, so they are able to identify structural biases whose functional forms are not a priori well understood. Impact charts are based on SHAP, an interpretability method initially designed to interpret predictions made by Machine Learning (ML) models, which is in turn based on Shapley values, an approach to assigning responsibility for economic outcomes to different factors. Although impact charts use techniques from the ML community, they are intended for use in general settings, whether ML is present or not. Impact charts provide valuable insights even when generated from aggregate data sets. Aggregate data sets typically provide the individuals whose data they are derived from an additional level of privacy as compared to the original unaggregated data. In this work, we relied predominantly on aggregate data from the U.S. Census Bureau, which is known to have a robust privacy program. We introduce and evaluate",1.0,https://dl.acm.org/doi/pdf/10.1145/3630106.3658965
821c7c8a2588b7ae9f1f80054af85ce7459e02c5,10.47576/2949-1916.2025.4.4.009,Региональные особенности цен на рынке жилья,2025.0,Regional and Branch Economy,Валерий Снежко; А. М. Ужахов; И. М. Ужахов,"Рынок жилья является ключевым индикатором социально-экономического развития регионов. Значительные диспропорции в ценах на первичном и вторичном рынках жилья на территории Российской Федерации прослеживаются более 15 лет. Целью исследования стала типизация регионов по средним ценам 1 кв. м общей жилой площади на первичном и вторичном рынках жилья по данным 2024 года и интерпретация состава кластеров с учетом социально-экономических характеристик субъектов. Для анализа использованы официальные статистические данные по ценам на жилье в 82 субъектах за исключением Москвы и Санкт-Петербурга, высокие цены на недвижимость в которых не являются характерными. За недостаточностью данных также не анализировался рынок недвижимости новых территорий. Методами исследований являлись кластерный анализ, визуализация и картографическое моделирование. Статистическая проверка качества полученной кластеризации подтвердила достоверность выделения шести кластеров, уровень значимости различий между кластерами был много меньше 0,05. Выявлены основные закономерности региональных различий на рынке недвижимости. Полученные результаты могут быть полезны для разработки дифференцированных подходов к регулировани",1.0,
c894e24053234d7cf094457d1d462afc0926b24e,10.1007/s41207-024-00538-8,Urban regeneration VS residential property characteristics: which is the most influential on property values?,2024.0,Euro-Mediterranean Journal for Environmental Integration,Xifilidou Agapi; Karanikolas Nikolaos; Spatalas Spyridon; Tsioukas Vassilis,"Urban regenerations have always been a tool towards the improvement of the urban environment through environmental integration and thereafter the quality of life of citizens. However, all regenerations do not have the same success and impact on the surrounding properties. In this paper, two major urban regenerations of Thessaloniki, the second largest city of Greece, are studied through the scope of their effect on property values in combination with the analysis of the effect of traditional property characteristics on values. This study was necessary to fill the gap of determining that sustainable urban planning and environmental integration are nowadays proven more important than property characteristics, when population gathering in urban areas is at its highest point. The current study applied geographically weighted regression, spatial autocorrelation and hot-spot analysis through the environment of ArcGIS to determine which are the factors that citizens seek in their residential area and if these factors are more important than their property characteristics. The findings of the study indicate that citizens always seek for additional urban green and urban quality even in area",1.0,https://doi.org/10.1007/s41207-024-00538-8
b30bfa280a7eb60ab11555ebe68a21456def243e,10.63900/dshjsv97,"In Search of a Conventional Urban Model for Explaining Factors Determining Residential Property Rentals and Prices in Lae City, Papua New Guinea",2020.0,Melanesian journal of geomatics and property studies,Michaella Otto; J. Babarinde; Paulus Motoro,"This paper reviews the literature on some conventional urban models that mirror existing patterns of residential property rentals and prices in cities, using Lae City in Papua New Guinea as a case study.The main purpose of the study is to identify which of the conventional models mirror the reality on the ground reflecting the decisions of residential tenants and landlords to live where they are in the city. There are serious concerns relating to spiraling residential property rentals and prices in Lae City that tend to push middle-income and low-income residents to go and live inthe squatter settlements surrounding the city where rents are affordable.Using stratified random sampling technique, arepresentative sample of Lae City residents consisting of 218 landlords and tenants was interviewed in conformity with Covid-19 pandemic restrictions in six (6) different residential districts of the city. These are China Town, Market, Top Town, Cassowary Road, Eriku and Kamkumung, in addition to selected real estate brokersof three popular real estate agencies operating in the city, urban planners working for Lae Municipality and the Department of Lands and Physical Planning (DLPP). The sa",1.0,https://pnguotjournals.unitech.ac.pg/index.php/mjgps/article/download/197/135
7f2bf023b3d439708384cd22f59dacc0eb34e47f,10.3390/land15010141,Urban Park Accessibility for the Elderly and Its Influencing Factors from the Perspective of Equity,2026.0,Land,Ning Xu; Kaidan Guan; Dou Hu; Pu Wang,"A well-designed layout for urban parks plays a crucial role in constructing livable cities and enhancing residents’ well-being. The provision of age-friendly park access is fundamental to building an elderly-friendly city. However, previous studies have lacked comprehensive analyses that integrate the distribution of the elderly population, park accessibility, park quality, environmental characteristics, and social equity within a unified framework. Specifically, the supply–demand imbalance mechanism underlying the spatial variations in accessibility has not been adequately addressed. This study employs an improved two-step floating catchment area (2SFCA) method, combined with Lorenz curves and urban park-adapted Gini coefficients, to examine the supply–demand relationship and allocation differences between the elderly population and parks at the neighborhood and community levels. The analysis highlights issues related to equity and accessibility and explores their spatial disparity and influencing factors. The key findings are as follows: (1) The classic 2SFCA model exhibits significant biases in evaluating park supply–demand relationships, accessibility, and equity at a fine-grai",1.0,https://doi.org/10.3390/land15010141
3a57fd406b1d115e6daf7efd1e4e8b65bb78a78d,10.35808/ijeba/821,Leveraging Value Adding Factors Through the Construction Development Cycle – Factors Changing the Property Value Landscape,2023.0,International journal of economics and business administration,Joseph Falzon; Claire; Rebecca,": Purpose: The economic value of residential homes is determined by an array of factors. Property value has taken on a new dimension as sustainability gains popularity in the real estate development industry. The housing market has been significantly impacted by the global COVID-19 outbreak and an increase in demand for domestic residences with access to outside spaces is a result of lockdowns and limitations. Homes that offer outdoor activities, natural ventilation, and a connection to nature are now prioritised by homebuyers. Thus, the economic worth of homes with gardens, patios, and spacious balconies has increased because of this change in preference. The aim of this research was to identify distinct key value-adding factors that could be implemented across the numerous stages of a construction project by individuals, at varied levels of expertise, within a construction development environment. This study examines important variables that affect local residential property value economically and sheds light on the importance for construction and renovation projects. Design/Methodology/Approach: The study explores highly desired property typologies and homeownership culture, as ",1.0,https://ijeba.com/journal/821/download/Leveraging+Value+Adding+Factors+Through+the+Construction+Development+Cycle+%E2%80%93+Factors+Changing+the+Property+Value+Landscape.pdf
73cf15fc74c9b503593e3262d2f2599b23431ff8,10.3390/su172310530,Fare Elasticity of Passengers in Mountainous Urban Rail Transit Considering Station Heterogeneity,2025.0,Sustainability,Qingru Zou; Yi Yang; Xinchen Ran; Jiaxiao Feng; Yue Xia,"Promoting sustainable mobility and socio-economic sustainability through demand management is critical for mountainous urban rail systems. This study investigates urban rail transit in mountainous cities, focusing on how passenger travel behavior responds to time-based pricing policies across different station types, with the aim of informing differentiated fare policy design. Using Chongqing—a city with pronounced mountainous terrain—as a case study, we classified stations into 12 categories based on 11 indicators, including road slope, bus transfer density, average housing price, and peak-hour train crowding within a 500 m radius. This classification was then combined with questionnaire data to quantify fare elasticity of departure time. The results show that high-value bus-transfer congested stations are concentrated in central urban clusters with dense bus networks, mitigating terrain constraints and encouraging active travel. In contrast, low-value pedestrian-transfer comfort-oriented stations are predominantly located on the urban periphery, where sparse road networks and steep terrain exert greater influence. Low-value pedestrian-transfer congested stations exhibit the highe",1.0,https://www.mdpi.com/2071-1050/17/23/10530/pdf?version=1764042640
9cc434f4150dcfa0ac932f84ccacef53747360f7,10.3390/rs17213531,Hourly and 0.5-Meter Green Space Exposure Mapping and Its Impacts on the Urban Built Environment,2025.0,Remote Sensing,Yan Wu; Weizhong Su; Yingbao Yang; Jia Hu,"Accurately mapping urban residents’ exposure to green space at high spatiotemporal resolutions is essential for assessing disparities and equality across blocks and enhancing urban environment planning. In this study, we developed a framework to generate hourly green space exposure maps at 0.5 m resolution using multiple sources of remote sensing data and an Object-Based Image Classification with Graph Convolutional Network (OBIC-GCN) model. Taking the main urban area in Nanjing city of China as the study area, we proposed a Dynamic Residential Green Space Exposure (DRGE) metric to reveal disparities in green space access across four housing price blocks. The Palma ratio was employed to explain the inequity characteristics of DRGE, while XGBoost (eXtreme Gradient Boosting) and SHAP (SHapley Additive explanation) methods were utilized to explore the impacts of built environment factors on DRGE. We found that the difference in daytime and nighttime DRGE values was significant, with the DRGE value being higher after 6:00 compared to the night. Mean DRGE on weekends was about 1.5 times higher than on workdays, and the DRGE in high-priced blocks was about twice that in low-priced blocks",1.0,https://doi.org/10.3390/rs17213531
7ef4eedbf9493a9c073f65b4c92444d77fbca1b7,10.1016/j.jenvman.2024.122852,Determinants of willingness to add pollinator beneficial plants to residential yards differ by plant type.,2024.0,Journal of Environmental Management,Amélie Y. Davis; J. Stoyko,"Yards (i.e. residential gardens) can provide suitable food and refuge for a variety of pollinators, but residents' decisions to make such changes to their yards depend on socio-economic characteristics of residents, their lifestyle and life stage, perceived or existing social norms surrounding yard appearance and associated care, as well as yard characteristics such as yard size and characteristics of the plants themselves. In this study, we randomly selected properties in two counties in southwestern Ohio along two gradients: parcel size and parcel valuation (as proxies for urbanization and income gradients, respectively). Two hundred surveys were deployed asking residents about their yard management practices, and their willingness to plant three pollinator beneficial plants (Asclepias syriaca, Echinacea purpurea, and wildflowers (multiple spp.)). One hundred and thirteen surveys were returned (57% response rate). For residents who did not have the aforementioned plants in their yards at the time of the survey, i.e. nearly two-thirds of the respondents, we used random forest models to uncover determinants of willingness to add the native plants shown in the survey in residential ",1.0,
f4bc80dc0fc2e84dcb6087bbed9633773fafd225,10.1109/access.2025.3568798,Deep-Learning Approach for an Analysis of Real-Estate Prices and Transactions,2025.0,IEEE Access,Cheng-Hong Yang; Borcy Lee; Yu-Da Lin,"Research on the study of houses, condominiums and buildings in Taiwan’s metropolitan areas continues to be an important area of research. In real estate forecasting and analysis, methods such as statistical analysis and questionnaire data collection are widely used. However, when multidimensional data is considered, these methods are time-consuming and inadequate. This study aimed to build a real estate forecasting model that can adapt to a changing environment. Data were collected from public government databases, the collected data were standardized for accurate clustering, an appropriate data clustering algorithm was applied to the standardized data, and cross-statistical analysis was performed to verify the adopted algorithm. We used a deep learning based on autoencoder algorithm to increase the accuracy of the clustering analysis. A double-bottom map particle swarm optimization (DBM-PSO) clustering algorithm was then used to determine the optimal clustering solution. Cluster analysis and deep learning were conducted on data collected from public websites to understand the factors that led to the sustained increase in housing prices in Taiwan over the past decade. The results o",1.0,https://doi.org/10.1109/access.2025.3568798
f4ea3a3d9eb41ac85106b96d60420a09bd5e2efa,10.1111/1475-6773.13018,Veterans Affairs Health Care-It is the Local Market.,2018.0,Health Services Research,K. Sheetz; C. Clancy,"Health care systems in the United States face significant financial and regulatory pressure to balance access to care with appropriate utilization of potentially expensive services. As the nation’s largest integrated delivery system, the Department of Veterans Affairs (VA) relies on Congressional appropriations and a fixed-cost business model for each medical center or outpatient clinic to provide health care to over 9 million veterans. In this issue of HSR, Hanchate and colleagues present an analysis for how external determinants (e.g., local unemployment) impact veterans’ utilization of the VA system. This study has several important findings. The authors report that VA utilization was sensitive to numerous external determinants. For many, such as increases in unemployment or the housing price index, absolute changes in utilization were small, but significant nonetheless. The largest change in utilization occurred in states that expanded Medicaid eligibility following passage of the Affordable Care Act. Here, aggregate VA utilization decreased by 9.1 percent among veterans’ ages 18–64. No significant changes were observed in nonexpansion states. In response to external determinan",1.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1475-6773.13018
43707ce9bf1a5edda9c026533304a23a876f7bc6,10.31390/gradschool_dissertations.3026,Restoring cultural capital through preservation in the Holy Cross Historic District,2014.0,,J. Hay,"This research analyses the social construction of a neighborhood’s history through the architectural narrative visible in its housing stock, critically engaging with modern practices of evaluation and interpretation of historic significance espoused by preservationists. Physical manifestations of the application of the National Historic Preservation Act and local regulations, in conjunction with efforts of preservationists as agents of recovery targeting the Holy Cross Historic District in New Orleans after the hazard events of 2005 reveal new and altered perceptions of the neighborhood through changes in space and structure. Methods include quantitative analysis of property values and demographics, (economic capital), and documenting of qualitative expressions of value, through sociopolitical characteristics such as historic significance, age, and access to resources through social networks including expertise and sweat equity (cultural and social capital). Physical evidence of change within the built environment, gathered through aerial and ground-level photography, as well as data from archival sources including tax rolls, local commission files, and property surveys, provided i",1.0,https://repository.lsu.edu/cgi/viewcontent.cgi?article=4025&context=gradschool_dissertations
8693ed425af8a70191afb3965f7e523795ec0a4b,10.1088/1742-6596/1899/1/012014,"Suitability of space utilization of settlement in coastal areas of abeli and nambo sub-district, Kendari City using spatial and non-spatial methods",2021.0,,H. Syaf; M. A. Rauf; M. Tadjuddah,"Land use for settlements needs to be regulated properly so that it is suitable with the city spatial plan by considering ecological balance aspect so that there is no deterioration in the land quality. Environmental damage can also occur because of the pattern of settlement development that exceeds the carrying capacity of the environment such as density, size and shape of settlements. Abeli and Nambo Sub-Districts, cover an area of 43.85 Km2 or 16.40% of the total area of Kendari city. it is a strategic area for urban development because it has a relatively wide location and it is directly facing the Banda Sea. This study aimed to: (1) analyze the level of suitability of land for settlements in the coastal areas of Abeli and Nambo Sub-Sub-Districts; (2) figure out of community perspective in the selecting the location of settlements in Abeli and Nambo Sub-Districts. The study used qualitative and quantitative research methods with descriptive qualitative analysis and spatial analysis. This study consisted of 8 variables namely slope, distance from the coast boundary, distance from river boundary, distance from tsunami prone location, distance from flood location, distance from man",1.0,https://iopscience.iop.org/article/10.1088/1742-6596/1899/1/012014/pdf
ee67e73f03cb165e84273ceb9ebb4e1afb65cd78,10.3390/ijerph20010390,"Examining the Human Activity-Intensity Change at Different Stages of the COVID-19 Pandemic across Chinese Working, Residential and Entertainment Areas",2022.0,International Journal of Environmental Research and Public Health,Shuang Ma; K. Cao; Shuang-Jin Li; Yao Luo; Ke Wang; Wei Liu; Guo-Hui Sun,"The COVID-19 pandemic has already resulted in more than 6 million deaths worldwide as of December 2022. The COVID-19 has also been greatly affecting the activity of the human population in China and the world. It remains unclear how the human activity-intensity changes have been affected by the COVID-19 spread in China at its different stages along with the lockdown and relaxation policies. We used four days of Location-based services data from Tencent across China to capture the real-time changes in human activity intensity in three stages of COVID-19—namely, during the lockdown, at the first stage of work resuming and at the stage of total work resuming—and observed the changes in different land use categories. We applied the mean decrease Gini (MDG) approach in random forest to examine how these changes are influenced by land attributes, relying on the CART algorithm in Python. This approach was also compared with Geographically Weighted Regression (GWR). Our analysis revealed that the human activity intensity decreased by 22–35%, 9–16% and 6–15%, respectively, in relation to the normal conditions before the spread of COVID-19 during the three periods. The human activity intensi",1.0,https://www.mdpi.com/1660-4601/20/1/390/pdf?version=1672215002
9505a193d47635f22db1b773af0498ec343b93f7,10.23895/kdijep.2010.32.4.35,Asset Prices and Consumption Dynamics in Korea,2010.0,,Kim; Y. Il,"This paper examines consumption dynamics in relation to asset prices in Korea. Empirical analysis based on the error correction model shows that personal consumption is affected by changes in asset prices but the consumption converges to the long-run level of consumption corresponding to the total income flow in two years. This adjustment in consumption implies that the consumption error, reflected in the error correction term, should have predictability for the future consumption growth during the adjustment period. It is found that the error correction term has a long-run predictability for consumption over up to about 3 years; thus, confirming the error correction model. It is also found that housing prices have larger effects on consumption compared with stock prices in Korea. In addition, the effects of income and asset prices on consumption show bigger effects during contractionary period than expansionary period in business cycles. This paper also analyzes effects of asset wealth that reflects changes in both price and quantity. It is found that asset wealth has a long-run effect on consumption in addition to total income as determinants of consumption. Since wealth effects ",1.0,https://doi.org/10.23895/kdijep.2010.32.4.35
c5c5b27f62f2583a9f92bd0ab0784d4a5aa176bb,10.5935/0034-7140.20190014,Non-Durable Consumption and Real-Estate Prices in Brazil: Panel-Data Analysis at the State Level,2019.0,Revista Brasileira de Economia,Victor Pina Dias; Érica Diniz Oliveira; J. Issler; Laísa Rachter,"Housing is an important component of wealth for a typical household in many countries. The objective of this paper is to investigate the effect of real-estate price variation on welfare, trying to close a gap between the welfare literature in Brazil and that in the U.S., the U.K., and other developed countries. Our first motivation relates to the fact that real estate is probably more important here than elsewhere as a proportion of wealth, which potentially makes the impact of a price change bigger here. Our second motivation relates to the fact that real-estate prices boomed in Brazil in the last five years. Prime real estate in Rio de Janeiro and Sao Paulo have tripled in value in that period, and a smaller but generalized increase has been observed throughout the country. Third, we have also seen a recent consumption boom in Brazil in the last five years. Indeed, the recent rise of some of the poor to middle-income status is well documented not only for Brazil but for other emerging countries as well. Regarding consumption and real-estate prices in Brazil, one cannot imply causality from correlation, but one can do causal inference with an appropriate structural model and prope",1.0,https://doi.org/10.5935/0034-7140.20190014
8f802cedf0194e383e1e3f20434319a8b5ef3cb4,10.3390/su17209300,"Analyzing the Relationship Between Built-Environment Factors and Safety Threat Reports in Cracow, Poland",2025.0,Sustainability,Zi-Xian Wu; Chen-Meng Wu; Lei Wang,"With the acceleration of urbanization, the coupling relationship between the built environment and urban safety hazards has become increasingly prominent. Irrational spatial structures and resource allocations may aggravate safety hazards and negatively affect residents’ quality of life, thus requiring urgent scientific evaluation and optimization. However, existing studies mostly focus on linear correlation analysis, which makes it difficult to reveal the complex nonlinear mechanisms among multidimensional environmental factors. Taking Cracow (Kraków), Poland as the study area, this research utilizes multi-source spatial data to quantify environmental features such as transportation, socioeconomic conditions, visual landscapes, and public services, in order to uncover their role in the formation of safety hazards. An XGBoost-based safety hazard prediction model is constructed, and SHAP interpretability analysis, together with two-dimensional partial dependence plots (2D PDPs), are introduced to systematically explore the synergistic gains, marginal effects, and resource allocation thresholds of key variables. The results indicate that variables such as average housing price, dista",1.0,https://www.mdpi.com/2071-1050/17/20/9300/pdf?version=1760953447
5a762049137ff4af2808bd8f1802e61447712eb1,10.4028/www.scientific.net/amm.368-370.2043,Study on the Relationship Between City and District Average Price by GAOT in Taipei,2013.0,,Hui-Chung Yeh; Tsu-Kuang Hsieh; Tai-Sheng Wang,"Since 2012, Taiwan enforces real price login, people can reference data and understand the changing price of various regions. The housing price of this study, therefore, is designated by unit price of trading contract. Taipei City, one of five municipalities in Taiwan, is found the unit price of trading contract is the most than others in an upward trend in its database from the third quarter of 2007 to the second quarter of 2012 (total twenty seasons). So, this study explores the relationship with others between unit price of trading contract overall in Taipei and unit price of trading contract of 12 districts. It is going to use autonomic architecture model with the ability of genetic algorithm operate tree (GAOT) to find out the best combination of variables and establish the relationship mode between each other. It also can assess overall real estate trends in Taipei City by important changing of districts. Research statistical analysis shows correlation coefficient (CC) for unit price of trading contract is 0.98 in Daan district and overall Taipei City. The result shows a high degree of correlation. After GAOT operator, besides, the establish assessment model could be found. I",1.0,https://doi.org/10.4028/www.scientific.net/amm.368-370.2043
ec00a40b6e2db64e3fa41ad635c40a386ed4f664,10.1108/ijhma-02-2026-0048,"Migration, residential housing prices and economic growth: quality of migrants matters",2026.0,International Journal of Housing Markets and Analysis,I. Semenenko,"
 
 The study aims to examine the behavior of house prices in developed economies and in emerging markets in response to migration flows by market type – gateway cities and national markets, as well as by origin of migrants.
 
 
 
 The author partitions migrant proxies, including flows and stocks, by type of country origin. Identification strategy is based on isolating the exogenous variation in house prices by applying traditional “shift share” instruments. Impact on both residential price levels and first order differences is reported for capital cities and national markets. Instrumental variables (2SLS) confirm results of OLS models, whereas spatial econometric models suggest correction in models with levels.
 
 
 
 Migration impact on housing prices differs by level of development. In advanced economies, migration effects are positive; positive impact of migration on housing valuation measured in levels is documented once structural error model (SEM) adjustment is performed. In emerging markets, inflows of migrants from emerging markets negatively impact housing valuations both in the short-term and in the long run. Results for both subsamples are stronger in regressions which ",1.0,
6e76780cbf3d9676153d69ac2e6283349ca3a1ea,10.61426/sjbcm.v2i1.82,FACTORS AFFECTING GROWTH OF HOUSING UNITS IN KENYA,2015.0,Strategic Journal of Business & Change Management,P. Kipkirui,"The importance of secure property markets is fundamental to Kenya’s economy particularly in light of the goals of Kenya Vision 2030, which sees real estate as one of the foundation pillars and a resource for the socio-economic development of Kenya or any economy. Globally, China is undergoing significant growth in property development, South Africa either, (the first by continental standards) has the same trajectory but at a decreasing rate. Forms of financing, types of risks, alternative building innovations and Property demand from commercial, residential and commercial is found to affect the development of housing sector. The study examined the recent trend in the development of housing units in Kenya for the last five financial years ending quarter 2, 2014. The study established and concluded that Property Loan market penetration is still low. The mortgage penetration though not the only key to house growth, is still very low either. High levels of inflation on the other side, currency and interest rates in 2011 and 2012 had negative impact on house prices, especially for those variable interest rate mortgages which were as high as 30%. The study also found that innovative buil",1.0,https://strategicjournals.com/index.php/journal/article/download/82/79
0075e16cbc9af664e37b61ad4ff49204b7c52cc1,10.53093/mephoj.1213893,Preserving Human Privacy in Real Estate Listing Applications by Deep Learning Methods,2023.0,Mersin photogrammetry journal,Yunus Emre Varul; Hilal Adıyaman; Tolga Bakırman; B. Bayram; Elif Alkan; Sevgi Karaca; R. H. Topaloğlu,"The images are important components of real estate applications on the internet to inform users. There are multiple rental and sale properties and many images of these properties on the internet and it is challenging to control the images of these real estate in terms of time, workload and cost. Considering the requirements of the problem, Deep Learning (DL), one of the Artificial Intelligence (AI) methods, offers ideal solutions. This study aims to distinguish images that contain humans using deep learning techniques. This will also aid not to violate the privacy of people according to the Law on the Protection of Personal Data in the image content used in real estate applications. For this purpose, firstly, a dataset of real estate images with and without humans called Real Estate Privacy (REP) dataset was created. The REP dataset was split into 70%, 20%, and 10% for training, validation, and testing, respectively. Secondly, the REP dataset was trained with Inceptionv3, ResNet-50, and DenseNet-169 architectures using transfer learning. Lastly, the performances of the architectures were evaluated by accuracy, precision, recall, and F1-score accuracy metrics. Experimental results i",1.0,https://dergipark.org.tr/en/download/article-file/2809953
51fec5dc61f239c8af9d175c8844af2033fc82c2,10.63642/1357-1419.1021,"Market calibration of ""cost"" models",2005.0,Journal of Property Tax Assessment  & Administration,R. Gloudemans; W. Nelson,"Although the benefits of regression analysis and market modeling are well known, a practical limitation of high concern to potential users is how to interface models, once developed, into their Computer Assisted Mass Appraisal (CAMA) systems. How do jurisdictions that have used the cost approach in the past interface models developed with SPSS or other statistical packages into their cost-oriented CAMA systems? This paper will demonstrate how the District of Columbia successfully addressed this issue to implement a highly improved and accurate valuation system. Like many jurisdictions, the District has utilized a combination of the cost approach and market trending for the appraisal of residential properties. The District’s CAMA software, Appraisal Vision ® by Vision Appraisal Technology, contains the cost approach and an interface with SPSS for modeling. While the District desired to implement MRA, it also wanted to maintain its familiar cost structure, both for value explanation purposes and in order to avoid system modifications or reprogramming. However, the District’s cost models are “hybrid” models that contain a combination of rates, size and depreciation curves, and multipl",1.0,
05d375330971b5777aae4d558b014fe54cdb4f35,10.4103/jme.jme_40_20,Spatial clustering and impact of household characteristics on under-five mortality in India: A secondary data analysis,2020.0,,J. Chaudary; S. Akshay; D. Reddy; Anusha Sharma; Nishu Jha; Pawna Kaushal; A. Malik,"Background: Under-five mortality rate (U5MR) is one among the best social indicators that identifies social development and well-being. Various risk factors are seen contributing to its increase. Aim: The present correlational study aims to identify the spatial clustering of U5MR in different states and union territories (UTs) of India and determines its association with household characteristics. Patients and Methods: The present study incorporates primary data for 29 states and 2 UTs (Delhi and Chandigarh) of India from the National Family and Health Survey-4 (2015–2016) for secondary data analysis. The data include the outcome variable which was the U5MR and predictor variables such as households with electricity; improved source of drinking water; toilet facility; solid fuels being used for cooking; anyone smoking and living in a pucca house. Primary data were analysed using GeoDa software, employing Univariate Local Indicators of Spatial Association, Spearman's correlation coefficient, ordinary least square (OLS) and spatial error model (SEM). Results: The study showed significant spatial clustering of high U5MR in six states and one UT, namely Rajasthan, Madhya Pradesh, Chhat",1.0,https://doaj.org/article/7a641e7977dc47d18dc4ffd03a4e43b8
889cef78b79e6b652dbdbdfe32662634133c161e,10.1371/journal.pclm.0000166,"Exploring the association between health, local area characteristics and climate action plans in the UK: Cross-sectional analysis using administrative data from 2018 and a citizen science ranking of climate action plans from 2021",2023.0,PLOS Climate,H. Brown; Scott Butterfield; Jessica Davies; Steven Dodd; A. Morris,"Local government has an important role to play in mitigation and adaption to climate change. In the UK, 82% of all emissions fall within the scope of local government. However, in UK climate legislation there is no statutory requirement for local government to address climate change. Eighty percent of local authorities have declared a climate emergency and developed climate action plans. The aim of this study is to explore the association between social vulnerability (poor health, financial resources, deprivation, house prices) and the quality of climate action plans as measured by a scorecard system created by Climate Emergency UK. We hypothesis that areas with lower levels of social vulnerability will have higher quality climate plans. We utilised a citizen science created dataset ranking local government’s climate action plans and administrative data on local area characteristics related to social vulnerability at the local authority level. Descriptive analysis and multivariate regression were employed. We found a very small but significant association between total weighted score, central government funding and average house prices. For the individual dimensions of the quality ",1.0,https://doi.org/10.1371/journal.pclm.0000166
a4fbe0dd8416e192c5c25cb20591800aaa1726b0,10.36948/ijfmr.2024.v06i04.25494,Planning Strategies for Slum Improvement in Bathinda City,2024.0,International Journal For Multidisciplinary Research,Bhushan Kumar Bansal; Meenu Chaudhary,"Slum improvement is a critical challenge faced by urban planners and policy-makers globally as burgeoning urban population outstrips the capacity of cities to provide adequate housing and services. This paper explores comprehensive strategies for the improvement of slums focusing on sustainable development, community involvement and policy innovation. By analyzing successful case studies from diverse urban contexts, the paper identifies key elements that contributes to effective slum upgrading including secure land tenure, provision of basic services, economic opportunities and participatory planning process. The research highlights the importance of integrating slum communities into the broader urban framework to ensure social equity and economic inclusiveness. Recommendations are provided for policymakers and urban planners on designing and implementing holistic slum improvement programs that can adapt to the unique needs of each community, fostering resilience and sustainable urban growth. This study also involves finding locational attributes of slums and assessment of present status of slums. The study is focused on the Bathinda Municipal Corporation and information for the an",1.0,http://dx.doi.org/10.36948/ijfmr.2024.v06i04.25494
e2de70c7171c641626731f33fa8ec2d44c6045bf,10.25236/ajbm.2021.030405,Real estate price model based on multiple linear regression and analysis of influencing factors of regional economic vitality development,2021.0,Academic Journal of Business & Management,Zihan Yan,"Using this statistical analysis method, which regards one variable as a dependent variable and one or more other variables as independent variables, the quantitative relations of linear or nonlinear mathematical models between multiple variables are established and analyzed by using sample data. Economic vitality is the regional support to its social economy. By analyzing the influencing factors of economic vitality, the evaluation index system of regional economic vitality is constructed. SPSS software is used to analyze the selected data, so as to judge the change of gross national product, which is of great significance for improving the efficiency of government policy implementation, stimulating urban economic vitality and realizing regional sustainable development. Taking Guangzhou as an example, through multiple regression analysis, this paper establishes a model of influencing factors from the perspectives of human capital level, government intervention, consumption, industrial structure and opening up, and puts forward an action plan to improve regional economic vitality. This paper studies the relationship between the average sales price of commercial housing and the sales",1.0,https://doi.org/10.25236/ajbm.2021.030405
7df6403cc193a2a8c827a0226bf7b6f51d167dc1,10.1016/j.exger.2026.113171,The relationship between intrinsic capacity and functional ability - identifying key environmental features to support healthy ageing.,2026.0,Experimental Gerontology,Jaro Govaerts; Zhao Yang; M. Visser; M. Huisman; Vincent François-Lavet; N. V. van Schoor; Laura A. Schaap,"BACKGROUND
Within the World Health Organization's (WHO) healthy ageing framework, older adult's functional ability (FA) is determined by the intrinsic capacity (IC), the living environment, and their interaction. This study aimed to identify candidate environmental factors in relation to FA, both directly and interactively with IC.


METHODS
Data from older adults (mean age = 69.4 years, SD = 7.5; 54.7% women) were obtained from the Longitudinal Aging Study Amsterdam (n = 1634). FA was assessed using a composite score of (Instrumental) Activities of Daily living ((I)ADLs). IC was measured using IC domain (vitality, locomotion, sensory, cognition, and psychology) scores (0-100). A total of 28 environmental factors was included. Random forest regression was used to model six-year FA, with IC domain scores and environmental factors as predictors.


RESULTS
Vitality, cognition and psychology IC domains showed the strongest direct associations with the predicted six-year FA score. Among environmental factors, perceived sufficiency of care, neighbourhood housing prices and airborne particulate matter (PM10) concentration levels demonstrated the strongest direct associations with the pred",1.0,https://doi.org/10.1016/j.exger.2026.113171
d878bc9e4cba01c320b4224158e5204ea90eeb42,10.2478/emj-2026-0003,Building energy performance and housing market regimes: evidence from EU countries,2026.0,Engineering Management in Production and Services,Dominykas Linkevičius; Laima Okunevičiūtė Neverauskienė,"Abstract In assessing developments in the real estate market, the energy efficiency of buildings is increasingly considered important from an environmental and economic perspectives. However, empirical evidence on the relationship between energy performance indicators and house price dynamics at the macro level remains mixed, suggesting that this relationship may vary across countries and contexts. This article examines whether and how building energy performance indicators are related to house price dynamics across European Union countries, focusing on short- and long-term relationships and cross-country heterogeneity. The study is based on panel data for 2010-2023, using house price indices as the dependent variable and several energy efficiency indicators, including final energy consumption in buildings and the share of renewable energy sources. Various econometric methods were applied, including panel cointegration analysis (FMOLS and DOLS), dynamic panel models (Arellano-Bond GMM), Granger causality tests, impulse-response analysis, quantile regression, threshold models, and exploratory country clustering. The results show that energy efficiency indicators are not strongly ass",1.0,https://reference-global.com/download/article/10.2478/emj-2026-0003.pdf
b44f73fc573200e80cf0ddb6774509ea739328be,10.1007/s40864-026-00268-w,Uncovering Travel Rhythms: A Spatiotemporal and Data-Driven Typology of Urban Rail Transit Stations,2026.0,Urban Rail Transit,Qushun Wang; Cuihua Zhang; Zhuanglin Ma; Haoran Xi; Hongwei Yang,"
 Smart card data (SCD) from Automatic Fare Collection (AFC) systems provide fine-grained insights into urban mobility rhythms. Yet most existing studies focus on static ridership, with limited attention to the relationship between station-level travel rhythms and the surrounding built environment. This study develops a spatiotemporal and data-driven framework to classify urban rail transit (URT) stations and examine their land-use determinants. Using two weeks of AFC data from 128 stations in Nanjing, China, a two-stage approach is implemented. First, a Gaussian Mixture Model (GMM) is applied to cluster stations based on weekday ridership profiles, yielding six distinct categories: residential oriented, employment oriented, hub comprehensive, spatial mismatched, predominantly residential mixed use, and predominantly employment mixed use. These categories reveal a clear transition from mixed and hub functions in the city center to residential-dominated stations in suburban areas. Second, a Random Parameter Logit (RPL) model is employed to assess the influence of socio-demographic, land-use, and amenity variables, capturing heterogeneity more effectively than the conventional Multin",1.0,https://link.springer.com/content/pdf/10.1007/s40864-026-00268-w.pdf
ee17f4a1b08c38e1b6a7fbc86b0591880d1da5e3,10.18187/pjsor.v21i4.4797,"A Novel Generated G Family for Risk Analysis and Assessment under Different Non-Bayesian Methods: Properties, Characterizations and Applications to USA House Prices and UK Insurance Claims Data",2025.0,Pakistan Journal of Statistics and Operation Research,M. Ibrahim; Abdullah H. Al-Nefaie; Nadeem Shafique Butt; G. Hamedani; Mujtaba Hashim; A. Aboalkhair; N. A. Ahmed; Noura Roushdy; H. Yousof; Noha Nabawy,"This study proposes a new and versatile family of continuous probability models known as the log-exponential generated (LEG) distributions, with particular emphasis on the log-exponential generated Weibull (LEGW) model as its prominent representative. By introducing an additional layer of parameterization, the family offers improved adaptability in shaping distributional forms, especially regarding skewness and heavy-tailed behavior. The LEGW formulation proves especially relevant for reliability data and for capturing rare but impactful events where asymmetry plays a major role. The work details the theoretical framework of the family through explicit expressions for its cumulative distribution function (CDF) and probability density function (PDF), alongside the corresponding hazard rate function (HRF). Several analytical characteristics are also investigated, including series representations and behavior in the extreme tail. To demonstrate practical value, the paper conducts risk evaluations employing sophisticated key risk indicators (KRIs) such as Value-at-Risk (VaR), Tail Value-at-Risk (TVaR), and tail mean-variance measure (TMVq) across multiple quantile levels. Parameter est",1.0,https://doi.org/10.18187/pjsor.v21i4.4797
3b7191ea3f341bbcb56408fb68ead611d7814d69,10.24940/theijst/2024/v12/i12/st2412-005,"Assessing the Environment, Social and Economic Impacts of Landfill Sites on Nearby Communities and Ecosystem in Cabalar Metropolis",2025.0,The International Journal of Science & Technoledge,Emmanuel Ewa Ekeng; I. E. Ibiang,"The Lemna landfill site in Calabar Municipality has raised significant concerns due to its proximity to residential areas and its impact on the surrounding environment and communities. This study comprehensively assesses the environmental, social, and economic impacts of the Lemna landfill site on nearby communities and ecosystems. The objectives of the research include identifying environmental hazards experienced by residents, investigating social and health challenges, and evaluating the economic implications on the local populace. Field surveys and questionnaire administration were used for the collection of socio-economic data. Other methods for data collection included oral interviews, direct physical observation and focused group discussion. Stratified, random, cluster and purposive sampling techniques were adopted, and a total of 400 copies of questionnaires were distributed and 342 were retrieved and used for analysis; 105 were administered in Lemna, 95 in Ikot Omin, which make up part of the communities in close proximity to the landfill site. The sample size was determined using Taro Yamane sample size determinant. The chi-square analytical statistics test was used to te",1.0,https://doi.org/10.24940/theijst/2024/v12/i12/st2412-005
fbd80736b8ce278fb94e52b36185d9dd90c38b09,10.22178/pos.41-1,Analysis of Current Empirical Studies on Transport Value-Added Effect and Proximate Housing Price Capture,2018.0,Path of Science,A. Aliyu; O. Kemiki; M. Bello,"Transport infrastructure is paraphernalia that helps in curtailing urban sprawl in municipal cities and it also lessens traffic overcrowding and air effluence. It equally promotes high-density development in addition to more affordable accommodation all over developed countries. This article reviews and evaluates the range of study outcomes established by the emerging frontier of knowledge delving on the capitalization effects of transport-oriented development on real estate prices. The effect of transport system services on accommodation price has been investigated from numerous viewpoints employing several rigorous statistical tools. Based on the findings of the existing literature, there are two broad kinds of impacts that closeness to a transport system can have on the value of housing accommodations: accessibility benefits (experienced in close proximity to transit services) might increase housing values, while nuisance qualities (experienced in transit-oriented facilities) could equally have a negative outcome on apartment prices. Owing to the contradictory nature of these simultaneous effects, findings from numerous empirical investigations have been opposing or open to deba",1.0,https://pathofscience.org/index.php/ps/article/download/565/585
2688814b8b98959676334acd789df663fc23027a,10.47709/ijmdsa.v5i1.7760,Determinants of Purchase Intention for Subsidized Housing in Flood-Prone Areas: Evidence from Bekasi Regency,2026.0,International Journal of Multidisciplinary Sciences and Arts,M. Maulana; Mulfi Sandi Yuda; Yoki Muchsam; G. Raspati,"The subsidized housing sector in Indonesia, implemented through the Housing Financing Liquidity Facility (FLPP) program, plays a crucial role in improving access to adequate housing for low-income households by offering affordable prices and long-term financing schemes. However, the realization of subsidized housing in Bekasi Regency has declined sharply, from 20,004 units in 2022 to 13,908 units in 2024, representing a decrease of 30.5 percent. This trend indicates a weakening of purchase intention, suggesting that affordability alone is insufficient to sustain demand.
This study aims to examine the effects of subsidized housing price, perceived flood risk, infrastructure quality, and building quality on housing purchase intention within the FLPP program in Bekasi Regency. A quantitative explanatory approach was applied using primary data collected through structured questionnaires from 100 respondents consisting of residents and prospective buyers of FLPP housing. Multiple linear regression analysis was employed to assess both partial and simultaneous effects of the independent variables.
The results indicate that subsidized housing price, infrastructure quality, and building qua",1.0,https://jurnal.itscience.org/index.php/ijmdsa/article/download/7760/5409
9827bd1e175e365bec82083b549e3084f63656a8,10.3390/land15061085,"Spatial Coupling Characteristics and Driving Mechanisms of Population–Land–Housing Based on Multi-Source Data: A Case Study of Guangzhou, China",2026.0,Land,Chun-Shan Zhou; Shu-Yuan Liu; Huiming Huang; Xiong He; Xiao-Die Yuan,"Against the backdrop of the transition of new-type urbanization towards high-quality development, the triple contradictions of population agglomeration, land constraints, and housing supply-demand imbalance have become increasingly prominent. The conventional binary framework of human–land relations can no longer meet the requirements of coordinated development within human settlement systems, creating an urgent need to examine the multi-system interactions among population, land, and housing in order to resolve spatial mismatch. Taking Guangzhou as a case study, this research integrates 2020 population census data, land-use data from the European Space Agency (ESA), housing-price data from the Anjuke platform, and multi-source data on related influencing factors, and conducts a systematic empirical analysis by combining coupling coordination analysis, a relative development model, and the geographical detector. The findings reveal that the coupling coordination level of population, land and housing in Guangzhou exhibits a concentric, ring-shaped distribution pattern with central agglomeration and peripheral decline. The relative development among the three systems can be classifie",1.0,https://doi.org/10.3390/land15061085
b1ca6daedffeea77b4cedf22b4a1b1f334b30bce,10.37082/ijirmps.v12.i2.230558,Implications of Residential Buildings in Proximate Distance to the Haags Bosh Landfill Site,2024.0,International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences,S. Eastman,"Twenty percent of the anthropogenic methane emissions worldwide originate from landfills. In addition, landfills typically contain a wide range of other gases, some of which are dangerous in low quantities. Through the liners' weaknesses, leachate can migrate to surface water or even groundwater, which is problematic because rehabilitating aquifers takes a long time. Landfill construction and operation have ecological repercussions that can result in altered landscapes, habitat loss, and fauna relocation. The socio-economic effects of landfills include the threats to public health posed by leachate-contaminated surface water or groundwater, the spread of litter into surrounding areas, and insufficient recycling efforts on the site. People typically list odors, noise, smoke, and annoyances like insects as reasons they are opposed to living near landfills. Numerous studies concluded that, depending on the actual distance from the landfill, home values are probably negatively impacted by landfills. In Guyana, several effects are reported due to the Haags Bosh Sanitary Landfill site being in the vicinity of residential and industrial areas. These include the spontaneous combustion of t",1.0,https://www.ijirmps.org/papers/2024/2/230558.pdf
7ad7709d9581c80938bb80f48809a9933a385c77,10.1101/2024.10.26.24316183,Neighborhood Influences on the Geography of Type 2 Diabetes in Malaysia: A Geospatial Modelling Study,2024.0,medRxiv,K. Ganasegeran; M. R. Abdul Manaf; N. Safian; L. A. Waller; F. Mustapha; K. A. Abdul Maulud; M. F. Mohd Rizal,"Type 2 diabetes (T2D) often exhibits long-standing disparities across populations. Spatial regression models can identify areas of epidemiological conformity and transitions between local neighborhoods to inform timely, localized public health interventions. We identified areal-level distributions of T2D rates across Malaysia and synthesized prediction models to estimate local effects and interactions of different neighborhood covariates affecting local T2D burden. We obtained aggregated counts of national level T2D cases data by administrative-districts between 2016-2020 and computed district-wise crude rates to correlate with district-level neighborhood demographic, socio-economic, safety, fitness, access to built-environments, and urban growth indicators from various national sources and census data. We applied simultaneous spatial autoregressive (SAR) models coupled with two-way interaction analyses to account for spatial autocorrelation and estimate risk factors for district-level T2D rates in Malaysia. The variation in spatial lag estimates of T2D rates by districts was influenced by the proportion of households living below 50% of the median income ({beta} = 0.009, p = 0.002",1.0,https://www.medrxiv.org/content/medrxiv/early/2024/10/27/2024.10.26.24316183.full.pdf
2af4d59a63c9e1659697b161d1d8a1d60a404bf8,10.1088/1755-1315/1353/1/012014,"Spatio Temporal Variations of Ridehailing Use on Campus Travel in Surabaya City, Indonesia",2024.0,IOP Conference Series: Earth and Environment,K. D. M. Handayeni; N. Rahma; A. Anindya,"Surabaya, one of Indonesia’s most densely populated cities, faces the challenge of massive urbanization and heavy traffic congestion. The city’s population of over three million, coupled with high mobility, has led to a surge in motorized vehicles, especially private cars. This is a direct consequence of the growth and development of the city. The prevalence of ride-hailing services in Surabaya has emerged as a common phenomenon, offering a solution to the rising demand for transportation services. By embracing ride-hailing services, the public has transitioned from traditional modes of transportation to digitally enabled, more convenient alternatives. From a spatial perspective, understanding the role of geographical factors is crucial in predicting ride-hailing trends. This research employs the Geographical Weighted Regression (GWR) method to analyze the factors influencing ride-hailing demand for students commuting to and from their campuses during different periods. Primary data collection is conducted through questionnaires to gather information about travel characteristics such as distance, time, cost, and frequency. Secondary data sources include data related to road density",1.0,https://doi.org/10.1088/1755-1315/1353/1/012014
2a6484b6880541094b4b22ee5c2a676ce13f2eea,10.3390/su18126183,Impacts of Urban Amenities on Socio-Spatial Differentiation: A Multiscale Analysis in Beijing,2026.0,Sustainability,Xian-Jia Jiang; Zhi-Hong Li; Peng Cheng,"With the growing focus on people-centered urban development sustainability in megacities, urban amenities have emerged as an important factor consistently associated with residential differentiation and restructuring. Understanding how it relates to the structure of social space is essential to advancing spatial equity. The study developed an analytical framework that integrates functional characteristics and supply patterns and applied Multi-scale Geographically Weighted Regression (MGWR) to examine how amenities shaped socio-spatial differentiation within Beijing’s Fifth Ring Road from 2015 to 2025. The results indicate that socio-spatial differentiation showed a rise followed by a decline across the three time points examined, yet its spatial pattern maintained a stable agglomeration characteristic of “high in the core area and low in the peripheral areas.” Significant differences exist in the roles of amenities across different attributes and scales. Market-driven factors, represented by amenity density and amenity diversity, typically exert their influence over larger spatial scales and are generally associated with spatial mixing and provide baseline opportunities for potenti",1.0,https://doi.org/10.3390/su18126183
de7ab1d0428f6604e47466d4b166816677aaf17d,10.1016/j.jenvman.2025.126952,Does the economy influence dynamic green exposure? A comparative study of spatial heterogeneity in visible greenery during running between high-and low-income groups.,2025.0,Journal of Environmental Management,Yanxin Jiang; Xinyu Xia; Xiao-Dong Liu,"Visible Green Space during Running (VGSR) has a significant impact on physical and mental health. However, current studies mostly focus on the green environment of specific static locations (such as residential areas and parks) from a spatial perspective, without fully addressing the issue of equitable access to green resources for dynamic groups with different socioeconomic characteristics. This study utilized running trajectory data to identify runners' trajectories and quantified the visible green environment and green landscape patterns within their running spaces. Based on property value quantiles (25 % and 75 %), the population was divided into different economic groups. The study explored the differences in visible green environments experienced by runners of varying economic levels and identified potential green environment solutions. The findings revealed the following: (1) Spatial heterogeneity exists in the equitable distribution of VSG during running across different income groups, showing a nonlinear numerical relationship and threshold effects with green landscapes; (2) Compared to micro-scale green morphology, macro-scale landscape pattern factors had a greater impac",1.0,https://doi.org/10.2139/ssrn.5131686
35817e1568467d660c0e8623286d835b8c69215f,10.1093/geroni/igae098.1501,MIDLIFE NEIGHBORHOOD SOCIOECONOMIC STATUS AND COGNITIVE FUNCTION IN LATE LIFE: DIFFERENCES BY RACE,2024.0,Innovation in aging,Greta Jianjia Cheng; Jeanine M. Buchanich; Tiffany L. Gary-Webb; Christina F. Mair; C. Shaaban; A. Rosso,"Abstract Evidence regarding the role of neighborhood socioeconomic status (NSES) as an upstream determinant of cognitive outcomes has lacked a life-course perspective and has relied on time-invariant NSES estimates. Compared to late-life, midlife NSES may be more relevant to cognitive outcomes, and associations with cognition may vary by race, reflecting the potential impact of structural racism. We examined racial differences in associations between midlife NSES and changes in cognitive function among older adults aged 70+. We included 341 (37% Black and 63% White) participants from the Pittsburgh site of Health, Aging, and Body Composition Study. Cognitive function was assessed repeatedly by Modified Mini-Mental State Examination (range: 80-100) throughout a 13-year follow-up. LexisNexis addresses from 1976-1985 were geocoded and linked to 1980 Census estimates (education, occupation, employment rate, household income, and home value). Midlife NSES (aged 49 to 58 years) for census tracts was calculated as the sum of z-scores of five estimates (range: -10.4 to 9.1, SD: 4.3). In linear mixed-effects models adjusted for census-tract and individual-level characteristics, midlife NSES",1.0,https://doi.org/10.1093/geroni/igae098.1501
a95f643197c2b660024c059c6bba8bd7401f03c6,10.54691/bcpbm.v33i.2729,Identifying Green Gentrification in Chongqing Central Park Based on Price Feature Model,2022.0,BCP Business & Management,Yuetong Zhou; Cheng-Han Xie,"As part of urban planning, urban greening plays a significant role in mediating urban development and improving quality of life for residents. However, studies have indicated that urban greening efforts may result in green gentrification, in which the creation of urban green spaces improve the quality of life of residents on the one hand, and increases the house prices in the surrounding area, thereby preventing low-income residents enjoying the ecological environment benefits. Even though, there are only a few studies that examine the connection between green gentrification and urban greenery in China. Moreover, there is a need for further investigation into the manifestations and characteristics of green gentrification and whether it always results in displacement. Therefore, we chose Chongqing Central Park as the study area. The results demonstrated that the house prices have increased exponentially over the past ten years in this area, the neighborhood is largely populated by middle- and high-income individuals, and residents who were more than 30 minutes away from the park no longer enjoyed it as often. By using a price feature model, ecological, architectural, and neighborhoo",1.0,https://bcpublication.org/index.php/BM/article/download/2729/2698
f6d50c3f01cbd467962f64aac0f6f064dfcf303c,10.24018/ejmed.2020.2.6.601,Impact of Women Empowerment on Infant Mortality Rate – Spatial Lag and Error Regression Model,2020.0,,K. Gill; P. Devgun; A. Kaur,"Background: Despite significant progress in recent decade, the burden of infant mortality in India remains high. As women is the main caregiver to the infant, women empowerment may be the important determinant of infant mortality rate which has received little attention. Therefore, the current study was planned to study the impact of women empowerment on infant mortality and to find the best fit regression model to predict infant mortality rate in various states of India. 
Methods: The National Family Health Survey (NFHS) 2015-16 data was used and infant mortality rate in various states and union territories of India was mapped. Regression analysis was performed with infant mortality rate as the dependent variable. The exploratory variables (Percentages) used for women empowerment were the literacy among women, women who opted for institutional delivery, women owning a house and/or land (alone or jointly with others), women having a bank or saving account and women having mobile phone that they themselves used. Ordinary Least Square Regression and spatial lag and error regression models were applied. Infant mortality rates predicted by using the best fit model were mapped. 
Results",1.0,https://ejmed.org/index.php/ejmed/article/download/601/339
2e8a79e9e27bc1e3561a8a28091a2690a217ef4c,10.1057/s41599-024-03641-x,Structural analysis of reverse mortgages in Taiwan,2024.0,Humanities and Social Sciences Communications,Hong-Ming Chen; Jing-Yi Chen,"This study investigated the framework of reverse mortgage (RM) programs in Taiwan, with a particular emphasis on the perspectives of both debtors and creditors. Employing the Lee–Carter model, the research estimated mortality rates in Taiwan while utilizing the theoretical framework to analyze various aspects of RM. House prices were assessed using the house price index as a foundational metric for estimating prices, along with lump-sum loanable amounts and RM considerations. Empirical analysis was conducted on debtors’ loans and incomes and financial institutions’ perspectives. The study delved into parameters such as maximum loanable amounts and income replacement ratios for debtors based on demographic factors such as sex, age, and occupation. Furthermore, the structure of RM programs was scrutinized from the viewpoint of financial institutions, considering factors such as rental income, remaining house value, creditor insurance protection, premium expenses, and creditor profitability. A series of simulations were performed to gauge the effects of different variables on the outcomes. The normal distribution of the house price index (HPI), as verified through the Anderson-Darling",1.0,https://www.nature.com/articles/s41599-024-03641-x.pdf
f70ecfeeaac97d50819602ec0687390ab719ff9d,10.26686/wgtn.17136059.v1,Make Yourself At Home: Can architecture enable a sense of home in Kuala Lumpur’s public housing?,2019.0,,N. Ibrahim,"Public housing in Kuala Lumpur was introduced by the government as a means of replacing informal settlements and providing housing for the lower income. Government subsidies often cover some of the costs of public housing to help keep it affordable and at the lower end of house prices. To help meet the low cost agenda, public house designs are often kept to a minimal standard in Malaysia, removing low income Malay dwellers from their ideal image of home. In the long run, signs of neglect in the public houses are reflected in the lack of care and maintenance from dwellers, vandalism and more.   This thesis proposes that good, homely architectural design practices suited to the dweller can help encourage emotional ties between dwellers (low income families) and the dwelling (public houses). Its aim is to investigate potential architectural design approaches to tackle such problems in future Kuala Lumpur public houses.  This raises the question of which homely architectural design strategies might be best utilised in the Kuala Lumpur public housing environment. The thesis begins by exploring the meaning of home in relation to both dwellers and dwelling before then identifying ‘homely’",1.0,https://openaccess.wgtn.ac.nz/articles/thesis/Make_Yourself_At_Home_Can_architecture_enable_a_sense_of_home_in_Kuala_Lumpur_s_public_housing_/17136059/1/files/31689065.pdf
73900be1d7b3db59ac68b02075d7cd86b6d23740,10.1108/ijhma-01-2025-0022,"Artificial intelligence algorithms to predict housing market prices in Surabaya, Indonesia",2025.0,International Journal of Housing Markets and Analysis,A. Njo; H. Sugiarto; Doddy Prayogo,"
Purpose
As the second largest city in Indonesia, Surabaya characterized by rapid economic growth and dynamic real estate markets, faces challenges in property valuation including data limitations, subjectivity and traditional methods that lead to inaccurate property pricing. To address these issues, machine learning (ML)-based methods offer the potential to provide more accurate predictions by leveraging historical data and identifying complex patterns. This study aims to analyze and evaluate the accuracy of various ML algorithms in assessing residential property prices in Surabaya.


Design/methodology/approach
An extensive data set about house prices is collected using hypertext preprocessor language (PHP) to scrap (Web scraping) from a property marketplace called Rumah123 in the capital of East Java Island, namely, Surabaya. This data is used to train and test multiple linear regression model and three popular ML models, i.e. artificial neural network (ANN), support vector machine (SVM) and classification and regression tree (CART), to predict house prices with 16 different features.


Findings
The model’s performance was evaluated using the linear correlation, mean absolute er",1.0,
3806fc2c86bf76336a6c67e9a3a8e9a75e650c4f,10.3390/buildings15162941,"Spatiotemporal Evolution and Driving Forces of Housing Price Differentiation in Qingdao, China: Insights from LISA Path and GTWR Models",2025.0,Buildings,Yin Feng; Yan-Jun Wang,"As China’s urbanization deepens, the spatial structure of residential areas and land use patterns has undergone profound transformations, with the differentiation of housing prices emerging as a key indicator of urban spatial dynamics and socioeconomic stratification. This study examines the spatial and temporal evolution of residential housing prices in Qingdao’s main urban area over a 20-year period, using data from three representative years (2003, 2013, and 2023) to capture key stages of change. It employs Local Indicators of Spatial Association (LISA) spatial and temporal path and leap analyses, as well as Geographically and Temporally Weighted Regression (GTWR) modeling. The results show that Qingdao’s housing price patterns exhibit distinct spatiotemporal heterogeneity, characterized by multi-level transitions, leapfrog dynamics and strong spatial dependence. The urban center and coastal zones demonstrate positive synergistic growth, while some inland and peripheral areas show negative spatial coupling. Evident is the spatial restructuring from a monocentric to a polycentric pattern, driven by shifts in industrial layout, policy incentives, and transportation infrastructure.",1.0,https://www.mdpi.com/2075-5309/15/16/2941/pdf?version=1755605625
c1649012cb9b02e1caffbaebc042505f59a446f3,10.3390/land14081552,Beyond a Single Story: The Complex and Varied Patterns of Park Accessibility Across China’s Emerging Cities,2025.0,Land,Meng-Qi Liu; Toru Terada,"China’s rapid urbanization has driven tremendous socioeconomic development while posing new forms of social–spatial inequalities that challenge environmental sustainability and spatial justice. This study investigates urban park-accessibility patterns across 10 s-tier provincial capital cities in China, examining how these patterns relate to housing-price dynamics to reveal diverse manifestations of social–spatial (in)justice. Using comprehensive spatial analysis grounded in distributive justice principles, we measure park accessibility through multiple metrics: distance to the nearest park, park size, and the number of parks within a 15 min walk from residential communities. Our findings reveal significant variation in park accessibility across these cities, with distinctive patterns emerging in the relationship between housing prices and park access that reflect different forms of social–spatial exclusion and inclusion. While most cities demonstrate an unbalanced spatial distribution of parks, they exhibit different forms of this disparity. Some cities show consistent park access across housing-price categories, while others display correlations between high housing prices and su",1.0,https://www.mdpi.com/2073-445X/14/8/1552/pdf?version=1753769438
692a81de8e97be9c90aa3bc1994e32a920647be4,10.3389/fpubh.2025.1587358,Spatiotemporal heterogeneity of the association between socioeconomic development and birth rate: a geographically and temporally weighted regression modeling study in China,2025.0,Frontiers in Public Health,Yu Yang; Rong-Xin He; Liming Li,"Background The birth rate is an important indicator of the health of the population. However, persistently low birth rate has become a pressing demographic challenge for many countries, including China. This has significant implications for sustainable population planning. Methods This study applied hot spot analysis and the spatiotemporal geographically weighted regression (GTWR) modeling, used panel data of 286 cities in China from 2012 to 2021 to explore the spatiotemporal heterogeneity of the relationship between the socioeconomic development and birth rate. Results The research has found that 2017 was an important turning point in China’s demographic transition. The hot spot analysis reveals that the birth rate hot spots are characterized by a multipolar kernel distribution, shifting from spatial diffusion to convergence, with the cold spots mainly located in the northeast. And the GTWR modeling found that the relationship between socioeconomic development and birth rate varies and change dynamically over space and time. Key findings include: (1) the negative impact of GDP per capita on birth rates has intensified; (2) housing prices exhibit both wealth and crowding-out effect",1.0,https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1587358/pdf
a51f76fd76555fedd91e244c6e58ebc3fac76346,10.1002/psp.70150,What Drives the Settlement of Highly Educated Talents in China? A Social Integration Perspective,2025.0,"Population, Space and Place",Linyun Zhang; Sheng Wang; Jiamin Xu; Wei Li,"In the current knowledge economy, regional strategies increasingly emphasize the attraction and retention of talent, making the settlement intentions of highly educated migrants a central concern for research and policymaking. While existing studies have largely examined explicit individual and regional factors, the influence of implicit dimensions, such as social integration, on long‐term settlement intentions remains insufficiently and systematically explored. To fill this gap, this study examines how social integration affects the settlement intentions of highly educated migrants in China. Drawing on 9202 highly educated respondents from the 2017 China Migrants Dynamic Survey, this study constructs a multidimensional index of social integration covering four dimensions: economic integration, social adaptation, behavioral adaptation, and identity recognition. It applies a binary logistic regression model, supplemented by endogeneity, robustness, and heterogeneity tests. The findings are fourfold. First, individual integration does not necessarily rise with regional economic development; in some developed advanced regions, high housing prices and living costs hinder integration. S",1.0,
50a8b50474ed1b5af4eccea786a90f296e7c2428,10.55824/jpm.v4i2.537,Utilization of Machine Learning for Property Price Segmentation and Prediction,2025.0,Society Jurnal Pengabdian Masyarakat,A. Andriansyah; Mulki Djenfik Dzulkarnain; Afni Izzah Afkarinah; Fadel Amili; Gilang Ramadhika; Nurvelly Rosanti; Siti Nurbaya Ambo; Yana Andharani; Mirza Sutrisno,"Advances in digital technology have encouraged the utilization of artificial intelligence, especially machine learning, in various sectors, including property price analysis. However, there are still many people who do not understand the basic concepts of this technology, so structured and applicable education is needed. To answer this challenge, an activity entitled “Utilization of Machine Learning for Property Price Segmentation and Prediction” was held which aimed to introduce and train participants in the application of machine learning to predict property prices. This activity consists of two main parts, namely webinars and workshops. The webinar focused on introducing the concepts of artificial intelligence, machine learning, and AI Project Cycle as the main method in analyzing house prices. Meanwhile, the workshop provided hands-on training to participants in building prediction models using Google Colab. This activity was carried out through a series of stages, starting from socialization, preparation of materials, pre-test to measure initial understanding, educational and practical sessions, to evaluation through post-test and filling in participant feedback. A total of 39",1.0,https://edumediasolution.com/society/article/download/537/257
6efecb1a3e88fca5c1a56a0ff559968117eeff57,10.3390/jrfm18040187,Foreign Investment and Housing Market Stability in Developing Economies: Empirical Evidence from Malaysia,2025.0,Journal of Risk and Financial Management,N. Ismail; M. Z. A. Karim; Helen X. H. Bao,"Sustainable property development in developing economies requires a careful balance between attracting foreign capital and maintaining housing affordability for local residents. While foreign direct investment (FDI) serves as a crucial engine for economic growth by enhancing productive capacity and international competitiveness, its effects on local housing markets remain inadequately understood in policy frameworks. This study examines how economic development strategies can be designed to harness FDI benefits while preventing residential market distortions in rapidly industrializing regions. Using Malaysia’s Kulim Hi-Tech Park and Batu Kawan Industrial Park as empirical cases, we analyze the relationship between foreign capital inflows and residential property prices from 2000 to 2022 through time-series regression analysis supplemented by stakeholder consultations. Our findings reveal that FDI significantly influences housing price dynamics in industrial zones, with both positive economic spillovers and challenges for housing affordability. The results demonstrate that targeted policy interventions—including affordable housing mandates, developer incentives, and strategic land u",1.0,https://www.mdpi.com/1911-8074/18/4/187/pdf?version=1743520199
61debbcb817597da7c1b94772ac9fd9560ecacfe,10.18778/1733-3180.36.01,Changes in the Affordability of Housing for Sale in Regional Capitals in Poland,2025.0,Space - Society - Economy,Maciej Koszel,"This article discusses the issue of the affordability of housing intended for ownership. The main objective of the paper is to assess changes in the value of the composite indicator of the affordability of housing for sale. The research covered the period from 2010 to 2023, which was dictated by the availability of data; at the time of creating the database, complete data for the year 2024 was still not available, while alternative sources of information significantly differed in the manner of data processing and considerably misrepresented research results. The spatial scope of the study covered 18 cities that are regional capitals (in the Polish nomenclature, these are so-called voivodeship cities).
The paper is structured as follows: the first part briefly discusses the housing situation in Poland, then the study presents a literature review on the methodology of research on housing affordability, and, in the final part, the results of the conducted research are shown and discussed. The paper also points to selected factors and determinants that have had a significant impact on changes in housing affordability. Significant limitations of the employed methodology were also addres",1.0,https://czasopisma.uni.lodz.pl/space/article/download/24169/26467
79e318a2733e4a965e06647abaa74e15edeac58f,10.61173/4yhsjv73,Research on Factors Influencing Housing Price based on Principle Component Analysis and Polynomial Regression,2024.0,"Science and Technology of Engineering, Chemistry and Environmental Protection",Jin Shi,"Housing price is influenced by natural, social, economic and other factors. Only by understanding and conducting specific research on these influencing factors can people grasp the laws of housing price movements. Focusing on this issue, this paper chooses the average unit housing prices and their influencing factors of 11 cities in Zhejiang Province in 2022 as the research objects, then establishes a model reflecting the quantitative relationship between housing price and influencing factors. Firstly, relevant data is collected from official websites, including housing prices and 10 main influencing factor indicators of above cities. Secondly, principal component analysis is used for data preprocessing, and five prominent indicators are selected by calculating the relative contribution rate of each factor. Then, by drawing functional images and solving correlation coefficients, a qualitative analysis is conducted on the basic algebraic structure of the regression model. The results indicate that all independent variables include independent terms and cross-terms. Subsequently, through polynomial regression, data matrices for both dependent variable and independent variables are se",1.0,https://www.deanfrancispress.com/index.php/te/article/download/1703/1227
d9de0ae75242c3ae0a2c1b2f102935a41fd6249c,10.48550/arxiv.2204.09050,House Price Prediction Based On Deep Learning,2022.0,arXiv.org,Yuying Wu; Youshan Zhang,"Since ancient times, what Chinese people have been pursuing is very simple, which is nothing more than ""to live and work happily, to eat and dress comfortable"". Today, more than 40 years after the reform and opening, people have basically solved the problem of food and clothing, and the urgent problem is housing. Nowadays, due to the storm of long-term rental apartment intermediary platforms such as eggshell, increasing the sense of insecurity of renters, as well as the urbanization in recent years and the scramble for people in major cities, this will make the future real estate market competition more intense. In order to better grasp the real estate price, let consumers buy a house reasonably, and provide a reference for the government to formulate policies, this paper summarizes the existing methods of house price prediction and proposes a house price prediction method based on mixed depth vision and text features. real estate related analyzes the development status the real estate at home M2, GRP, commonly existing resnet50 are used to preprocess the index attributes. The flow chart of the MVTs model is constructed, and the implementation steps of the proposed model are descri",1.0,http://arxiv.org/pdf/2204.09050
ee4a73efc5ad67b4400db23b09eeacbeb4812f86,10.3846/ijspm.2025.25137,Explainable AI-based mass appraisal: Insights from machine learning applications in Korea’s residential property market,2025.0,International Journal of Strategic Property Management,Woosung Kim; Insu Choi; Myounggu Lee,"Mass appraisal plays a pivotal role in real estate management, facilitating property tax assessments, mortgage evaluations, and urban planning across large geographical areas. In regions like Korea, where real estate markets are rapidly evolving, valuation models based on multiple linear regression are valued for their simplicity and interpretability but often fall short in capturing complex market dynamics. In contrast, machine learning (ML) models, while addressing non-linear relationships between property characteristics and market values and offering superior predictive performance, are often criticized for their “black-box” nature, which raises concerns over interpretability in transparency-critical domains like property tax assessments and policy planning. To address these concerns, this study investigates the application of Explainable AI (XAI) techniques in the mass appraisal of residential properties in Korea, integrating XAI methods with both multiple linear regression and random forest models. Using SHAP (SHapley Additive exPlanations) and PFI (Permutation Feature Importance) values, the study analyzes feature importance and predictive contributions, offering insights in",1.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/25137/13114
a3d6c640ddd044492b41999311b06dbe1b5ff52c,10.56892/bimajst.v6i02.364,"EFFECTS OF AIRCRAFT NOISE ON RESIDENTIAL PROPERTIES RENTAL VALUE AROUND MAIDUGURI INTERNATIONAL AIRPORT, BORNO STATE, NIGERIA",2022.0,BIMA JOURNAL OF SCIENCE AND TECHNOLOGY (2536-6041),"Bitrus Ayuba; Andesikuteb YAKUBU ALI,; Isaac INNOCENT IKO; Donatus ADAMU LUCAS","This study examined the effects of aircraft noise on residential properties rental value around Maiduguri International Airport Area in Borno State. The data was collected through the administration of 363 structured questionnaire to selected household heads residing in the neighbourhoods around the airport. It utilized mean ranking, the simple regression analysis toanalyze the data collected. The result of the simple regression analysis revealed that aircraft noise negatively influences residential property value around the airport and that aircraft noise does not really depreciate residential property rental value, the noise level for the day-eveningnight time depends on frequency of flights as well as distance from the airport and the time of the day and also showed that rental values of residential properties in the study area fall within the range of 50,000 Naira and below for one room apartment. The study also found that tenants are willing to stay in the neighbourhood due to the presence of Air Force Base (security personnel) in the area. The study therefore recommends among other things, that investors and propertydevelopers embarking on property development should take int",1.0,https://doi.org/10.56892/bimajst.v6i02.364
c6422899339cf7b2a676fba4b8e0f160eca4136b,10.2139/ssrn.3724568,Documenting Loss Aversion using Evidence of Round Number Bias,2020.0,Social Science Research Network,Stephen L. Ross; Ting-Yu Zhou,"Many studies document loss aversion in the housing market, where expected losses lead to higher sales prices. However, exposure to expected losses may correlate with unobservables that influence housing prices. Under the assumption that multiple psychological biases appear together, we estimate loss aversion by identifying sellers who appear psychologically biased by exhibiting focal point or round number bias in their choice of mortgage amount at purchase. Using both difference-in-differences and regression discontinuity approaches, we find evidence of loss aversion on sales prices based on a stronger correlation between loss and sales price for our subsample of sellers who exhibited round number bias, but our estimated effects are substantially smaller than the results that arise from directly estimating the effects of expected loss on the sales price. In addition, lumpy sellers are less likely to sell relative to the control group. We show that expected loss correlates strongly with predetermined mortgage, housing unit, and census tract attributes, but the interaction between a round mortgage amount and expected loss exhibits far fewer failures of balance. Further, the magnitude",1.0,https://doi.org/10.2139/ssrn.3724568
6e370b66e4904206e1a16baefa54639bb731e7cf,10.1108/igdr-05-2024-0062,Economic policy uncertainty and inflation expectations of the households – insights from survey data,2025.0,Indian Growth and Development Review,D. Rooj; Anurag Banerjee; R. Sengupta,"Purpose
This paper aims to analyze how economic policy uncertainty (EPU) affects inflation expectations. The study uses unit-level observations of 3 months ahead and 1-year ahead inflation expectations from the Reserve Bank of India’s Inflation Expectations Household Survey (IESH) to explore the variations in consumer confidence across several cities in India.

Design/methodology/approach
This paper uses unit-level observations of 3 months ahead and 1 year-ahead inflation expectations of the households from the Reserve Bank of India’s IEHS between September 2008 to July 2024. It uses a fixed effects regression exercise to examine the impact of EPU on the households’ inflation expectations.

Findings
The study results show that EPU negatively and significantly impacts consumers’ short-term inflation expectations. Heterogenous analysis concerning gender reveals that impact of EPU on inflation expectations is higher for women than men. Further, respondents in the working-age population and greater income uncertainty are more sensitive to changes in EPU on inflation expectations. A disaggregated analysis is also conducted to analyze the impact of EPU on several disaggregated indicators",1.0,
7730a8d59ef1c2edf4280f2bceef6ca8c1972812,10.3390/land12061116,How Migration Behavior Affects the Contracted Land Disposal Methods of Rural Migrants in China: An Analysis Based on the Perspective of Geographical Differences,2023.0,Land,Yi-Hu Zhou; Ting Fang,"Migration leads to the separation of rural people from their contracted land and, thus, affects the human–land relationship, agricultural production, and food security in China’s rural areas. Using spatial autocorrelation analysis, GeoDetector analysis, and geographical weighted regression, the mechanism by which the migration behavior of the rural population impacts their contracted land disposal methods was analyzed in this study, and the spatial heterogeneity of this mechanism was further revealed from the perspective of geographical differentiation. The results of this study show that: (1) Chinese rural migrants exhibit pronounced geographical differences in the disposition of contracted land. The spatial distribution patterns of family operation, subleasing, and abandonment are different. (2) Migration behavior contributes to the differences in contracted land disposal methods across regions. Educational attainment, the number of cities migrated to, and the house purchase rate significantly contribute to the geographical differences among all three contracted land disposal methods. (3) The mechanisms by which migration behavior influences rural migrants’ land disposal decision",1.0,https://www.mdpi.com/2073-445X/12/6/1116/pdf?version=1684837229
9875dd434e8bd2cbb5c0a3885307d2fbbab308e6,10.1186/s12889-025-26089-3,Structural factors of disability and ageing: spatial patterns in Mexico,2026.0,BMC Public Health,R. García-Chanes; Diego Terán-Paéz; Jim Garcia; Konstantinos Roussos; Carmen García-Peña,"Mexico is undergoing rapid population ageing, but its pace and intensity vary markedly across regions. This process unfolds amid deep social and economic inequalities, particularly in rural and marginalized areas where limited access to healthcare and social protection increases the risk of chronic disease, disability, and dependence. To examine how demographic ageing, socioeconomic, and health conditions relate to disability prevalence among older adults at the municipal level, emphasizing the structural and territorial factors that constrain healthy ageing. Using data from the 2020 Population and Housing Census and 2015–2019 mortality records, a municipal composite ageing index was developed to capture both the magnitude and structure of ageing. Spatial analytical techniques—Moran’s I, Local Indicators of Spatial Association (LISA), and Geographically Weighted Regression (GWR)—were applied to identify spatial dependence and local variation. Bivariate Moran’s I indicated limited global autocorrelation between ageing and disability, but LISA revealed pronounced local clusters. Ageing was more advanced in northern and central Mexico, whereas disability concentrated in the south and ",1.0,https://doi.org/10.1186/s12889-025-26089-3
3023bb0c59b92724510c2baa43d5c0cef7038262,10.1016/j.jhe.2018.09.010,Proximity to a water supply reservoir and dams: Is there spatial heterogeneity in the effects on housing prices?,2019.0,Journal of Housing Economics,Jeffrey P. Cohen; Joseph J. Danko; Ke Yang,"An understanding of the spatial variation in the impacts of living near reservoirs, dams, and undevelopable land is important in explaining residential property values. While there is a body of literature on the effects of proximity to dams and reservoirs on housing prices, little known research attempts to determine if various individual houses are impacted differently depending on their locations and years of sale. We examine properties in Barkhamstead, Connecticut that sold between 2001 and 2015. We utilize non-parametric regression techniques to allow for the possibility that the major reservoirs, dams and undevelopable land areas, affect various house prices differently, depending on their locations and when they are sold. We find that for the most part, proximity to dams leads to lower housing sale prices, with the magnitudes of these effects varying across geographic space and over time. A difference-in-differences approach indicates that the willingness to pay for distance from the dams decreased after the most recent housing crisis. In general, undevelopable land area is valued as an amenity in this rural town. The signs of the effects of proximity to the reservoir vary - ",1.0,https://doi.org/10.2139/ssrn.2839006
b5436b1245aaa29cbbdbff01ce5b815d942ebf83,10.3233/faia200730,The Mechanism Analysis of the Accelerator for Support Vector Regression Based on Data Partition,2020.0,Fuzzy Systems and Data Mining,Yun-Sheng Song; Fang-Tao Li; Jianyu Liu; Juao Zhang,"Support vector regression is an important algorithm in machine learning, and it is widely used in real life for its good performance, such as house price forecast, disease prediction, weather forecast, and so on. However, it cannot efficiently process large-scale data, because it has a high time complexity in the training process. Data partition as an important solution to solve the large-scale learning problem mainly focuses on the classification task, it trains the classifiers over the divided subsets produced by data partition and obtain the final classifier by combining those classifiers. Meanwhile, the most existing method rarely study the influence of data partition on the regressor performance, so that it is difficult to keep its generation ability. To solve this problem, we obtain the estimation of the difference in objective function before and after the data partition. Mini-Batch K-Means clustering is adopted to largely reduce this difference, and an improved algorithm is proposed. This proposed algorithm includes training stage and prediction stage. In training stag, it uses Mini-Batch K-Means clustering to divide the input space into some disjoint sub-regions of equal s",1.0,https://doi.org/10.3233/faia200730
9bd166858991ed89e221a8fb56169533ad4f2ece,10.1088/2515-7620/ae5589,How visual environment affects property prices: nonlinear assessments based on machine learning and street view big data,2026.0,Environmental Research Communications,JiaJing Wang; Ning-Yuan Fei; Hao Wu; Qianqian Lin; B. Dewancker,"Property prices and their influencing factors consistently draw significant societal attention. Identifying key drivers of property prices helps governments and policymakers optimize real estate market regulation strategies. Despite its importance, the quantifiable value of natural landscape visibility has received limited attention in existing research, especially regarding how its specific visual characteristics influence property prices. To address this, our study developed a fusion analysis framework based on street view big data, utilizing a Gradient Boosted Decision Tree (GBDT) model (R2 = 0.47, MAE = 7052.82) to systematically explore the nonlinear impact of various visual environment factors on property prices in Shanghai’s central urban real estate market in 2024, aiming to identify the potential value of natural landscape visibility in the urban housing market. The results show that visual environment factors contribute 43.47% to property prices. Among these, the visibility of grassland landscapes and vegetation cover emerged as critical variables influencing property values. The study also revealed significant nonlinear characteristics and threshold effects in how visual",1.0,https://iopscience.iop.org/article/10.1088/2515-7620/ae5589/pdf
4505071928eb7c047e48b89c95c0aaafba9a5550,10.1515/geo-2022-0613,"Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China",2024.0,Open Geosciences,Shuang Cheng; Wuxin Liu; Wangyang Jiang; Chen Li,"Abstract It is crucial for social sustainability that adolescents have access to social sports services fairly. However, there are few research studies on requirements for sports facilities and spatial accessibility of adolescents at a fine scale. Further, identifying the heterogeneity of the relationship between spatial accessibility and other factors and their scales simultaneously would be conducive to reveal the variations of spatial accessibility effectively under the potential scale effect. This research aims to explore the heterogeneity of spatial accessibility to sports facilities at a fine scale in Changsha, China. The Gaussian-based two-step floating catchment area model is first employed to evaluate spatial accessibility. Then, multiscale geographically weighted regression (MGWR) is applied to examine the relationship between spatial accessibility and its associated factors. The mean accessibility in Yuelu District (0.158) is the highest as well, and the standard deviation (0.236) is also the highest one. Both the accessibility (0.019) and its standard derivation (0.029) in Furong District are the lowest. The results show that there is a lack of balance of spatial access",1.0,https://www.degruyter.com/document/doi/10.1515/geo-2022-0613/pdf
792de905e26db923cc495f4df0c6cb502f55aaf2,10.1371/journal.pone.0335722,RETRACTED: Optimization of house price evaluation model based on multi-source geographic big data and deep neural network,2025.0,PLoS ONE,Xuan Wang; Xuan Li; Haiyan Li,"The real estate market requires effective and precise house price prediction, as conventional models often face difficulties in generalization, computational efficiency, and interpretability. The research problem is addressed by introducing the House Price Evaluation Model (HPEM), which utilizes a hybrid deep learning network for analyzing multi-source geographic data. The network integrates the attention mechanism with spatial feature extraction, and a bat optimization algorithm is used to improve explainability and accuracy. The gathered properties are processed using normalized techniques to convert unstructured data into structured data, which directly improves the overall prediction accuracy. The bat-optimized attention mechanism with spatial networks dynamically arranges high-impact features to effectively address unstable feature importances, computation inefficiency, and poor generalization issues. In addition, the echolocation-inspired approach explores optimal solutions by balancing exploration and exploitation, thereby minimizing the deviation between the outputs and reducing training time by 30% compared to existing methods. The efficiency of the system is then evaluate",1.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0335722&type=printable
f6fc45c0ef3285996689674c9cfbb3c8e6491820,10.54254/2755-2721/32/20230188,Research on feature coding theory and typical application analysis in machine learning algorithms,2024.0,Applied and Computational Engineering,Peng-Xiang Wang; Kai Xiao; Li-Hao Zhou,"Nowadays, the world is still in the environment of economic depression. In order to promote economic recovery, improve Relations of production and production efficiency, stimulate consumption expansion and upgrading, and accelerate industrial transformation and upgrading, problems such as industrial upgrading need to be solved urgently. Solving the above problems requires more useful tools, and artificial intelligence is one of them. Machine learning is the key to distinguishing artificial intelligence from ordinary program code. Unlike people learning knowledge, machine learning has its own unique language algorithms and behavioral logic. Machine learning, as a technology active in the field of artificial intelligence in recent years, specializes in studying how computers learn, simulate and realize part of human learning behavior, so as to provide data mining and behavior prediction for humans, to obtain new knowledge or skills, or to strengthen the original basic ability of machines. In this study, a variety of common coding algorithms and learning strategies in machine learning are discussed, supervised learning algorithms are selected as examples in the learning strategies, mo",1.0,https://www.ewadirect.com/proceedings/ace/article/view/9923/pdf
812fa5e0891b51b647184994ecedbf2306ac51ea,10.46793/ebm24.417m,HOUSING PRICE PREDICTION USING XGBOOST AND RANDOM FOREST METHODS,2025.0,Zbornik radova,Ljiljana Matic; Zoran Kalinić,"Real estate prices often experience significant variations due to various reasons such as shifts in land value and local infrastructure development. An application for predicting housing prices, taking into account the property and neighborhood characteristics and infrastructure, is essential for providing customers with accurate insights into where and when to invest in property. This research focuses on apartment price prediction in real estate market of Belgrade, employing advanced machine learning algorithms, XGBoost and Random Forest, to estimate property values. These algorithms are highly effective at processing large datasets and uncovering complex relationships among key predictors. Data preprocessing plays a crucial role in ensuring model accuracy. This process includes data cleaning, handling missing values, and addressing outliers, while categorical features are transformed through one-hot encoding to enhance model performance. Both XGBoost and Random Forest models are applied to predict apartment prices, providing valuable insights into the most influential factors in the market. A comparative analysis of these methods not only highlights their respective strengths and",1.0,
e3fae4b613cb00e51d86af175cdd8ab46c0ea644,10.11648/j.ijdsa.20180405.13,Investigating the Existence of a Bubble in the Kenyan Real Estate Market,2018.0,,Pauline Njoroge; J. Aduda; C. Mugo,"In the Kenyan real estate industry, laws of economics seem to be violated. The demand for houses has been increasing tremendously despite the oversupply. This violates the laws of economics indicating a possibility of a real estate bubble. The study aimed at estimating short term and long term real estate price dynamics in Kenya using co-integration tests. Secondly, the study aimed at identifying the presence of a Kenyan real estate bubble using the forward-recursive Generalized Augmented Dickey-Fuller test (GSADF) and finally measured the size of the bubble at a given time relative to other key macroeconomic variables. The study utilized quarterly data on house prices and rental prices in Kenya and macroeconomic determinants from the year 2004 to 2017 September. Stationarity test revealed that the variables were stationary in their first difference I (1). Cointegration test revealed that there was no long term and short term house price dynamics between house prices and the macroeconomic variables at a lag of 4 determined through AIC, SIC and HQ criterion. Again a Granger causality test was performed and the results revealed that the macroeconomic variables did not Granger-cause h",1.0,https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20180405.13.pdf
719da1ab6dc879c18a913b430d30392463307ad8,10.35808/ersj/2525,How Effective is Macroeconomic Imbalance Procedure (MIP) in Predicting Negative Macroeconomic Phenomena?,2021.0,EUROPEAN RESEARCH STUDIES JOURNAL,Krzysztof Biegun; J. Karwowski; Piotr Luty,": Purpose: The evaluation of the predictive power of Macroeconomic Imbalance Procedure (MIP) indicators is crucial for coordinating the economic policies of the EU countries. MIP is one of the pillars of the economic crisis prevention procedure. Design/Methodology/Approach: Using the Bayesian model averaging (BMA) framework, we compare different models where lagged MIP indicators try to explain several macroeconomic variables associated with crises. Findings: The results show that the importance of MIP indicators between 2001 and 2017 was diversified. In the case of annual real GDP growth, including a 1-year lagged house price index, nominal unit labor cost, real effective exchange rate (1-year change), and export market share in the model improves the model's explanatory power most. For explaining inflation rate, export market share (again), and house price index is valid. Practical Implications: The construction of the MIP procedure should be simplified, as not all indicators have a fundamental capability of predicting excessive imbalances which result in crisis events. Indicators are relevant to the current economic priorities of the EU, which do not have a significant capacity ",1.0,https://doi.org/10.35808/ersj/2525
adfbb4e413a9e73aa0535da6a86a9561f6902492,10.3390/buildings14103130,Has Cross-City Commuting Promoted Housing Purchases among the Workforce within Metropolitan Areas?—An Empirical Analysis from Micro Survey Data from China’s Three Major Metropolitan Areas,2024.0,Buildings,Zheng-de Fan; Chengdong Yi; You-Rong Wang; Ye-Qi Cao; Yu-Fei Liu,"The ability of the cross-city commuting labor force to obtain housing has a profound impact on the development of the housing market and the enhancement of social welfare, but whether cross-city commuting has facilitated housing purchases remains to be verified However, the research on whether cross-city commuting behavior promotes labor force housing purchase in metropolitan areas is still lacking, especially in China, where the culture of buying houses is relatively special. This article used field survey data from the 2023 China Metropolitan Area Occupation and Housing Status Sampling Survey to empirically analyze whether cross-city commuting has facilitated housing purchases within metropolitan areas. The analysis was conducted by constructing a baseline model, a mediation effect model, and a subsample regression model. The results show that the cross-city commuting facilitated housing purchase within metropolitan areas, and the location preference is to purchase a house with a distance of 20–40 km from the workplace, but the contribution of the cross-city commuting to multi-suite purchases is relatively low. Mechanism analysis shows that compared to the workers who work and li",1.0,https://doi.org/10.3390/buildings14103130
c4530550ffe4c8833ae5f7ce65c15cde4d19767c,10.4236/me.2015.610108,"The Analysis of the Housing Markets in Beijing and Washington, 2005-2014",2015.0,,Xin Yao,"Since the economic 
reform of the late 1970s, China has achieved a magnificent economic growth. China’s 
socialist market economy has become the world’s second largest economy by 
nominal GDP, reaching 10,380 billion dollars and holding a 13.43 percent share 
of global GDP. The dramatic growth of economy also has boosted the development 
of Chinese housing market. But the question remains: Is the Chinese real estate 
market developing in a positive way? This paper first compares the housing 
prices in Washington and Beijing using hypothesis test for population means and 
linear regression model. It is found that despite China’s low housing price in 
early years, the average housing prices are almost the same in Beijing and 
Washington area. The most reasonable proposal is that the personal income in 
Beijing is higher. However, the data and the line chart illustrate that the 
opposite is actually true. Therefore, in order to further explore the reasons 
behind the high housing prices in Beijing, the paper then statistically analyzes 
a series of factors, including per capita income, limited land resources, city 
functions, unbalanced regional development, the influence of cultural ",1.0,https://doi.org/10.4236/me.2015.610108
7b5fd87c28c6295cbb1446d83f58278d8ae025d2,10.30598/barekengvol19iss1pp107-118,MODELING HOUSE SELLING PRICES IN JAKARTA AND SOUTH TANGERANG USING MACHINE LEARNING PREDICTION ANALYSIS,2025.0,BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN,Sugha Faiz Al Maula; Nicoletta Almira Dyah Setiawan; Elly Pusporani; S. Z. Jannah,"The increasing demand for housing in urban agglomerations, particularly in areas like Jakarta, has made homeownership a significant challenge for many, especially first-time buyers and the lower-middle class. Post-pandemic shifts have further influenced housing preferences, driving interest towards suburban areas with green spaces. Despite government efforts through mortgage subsidy programs, affordability remains a concern, particularly in peripheral regions. This study aims to analyze housing prices in various Jakarta regions using machine learning models, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), Light Gradient Boosting Machine (LGBM), and Random Forest. A dataset of 554 house prices from West Jakarta, South Jakarta, Central Jakarta, and South Tangerang was used. The analysis focused on key predictors like land area, building area, bedrooms, and carports, with R² and Mean Squared Error (MSE) metrics evaluating model performance. Results showed that LGBM and Random Forest outperformed others with 0.8 R2 and low MSE, with building and land area as the most significant factors influencing prices. The study concludes that property size is a primary",1.0,https://ojs3.unpatti.ac.id/index.php/barekeng/article/download/12906/9511
9960c6a38ff73ff0d2c4ef194a52833524b69a57,10.3390/stats8010012,Smart Renting: Harnessing Urban Data with Statistical and Machine Learning Methods for Predicting Property Rental Prices from a Tenant’s Perspective,2025.0,Stats,F. Louzada; K. J. C. C. de Lacerda; P. H. Ferreira; N. D. Gomes,"The real estate market plays a pivotal role in most nations’ economy, showcasing continuous growth. Particularly noteworthy is the rapid expansion of the digital real estate sector, marked by innovations like 3D visualization and streamlined online contractual processes, a momentum further accelerated by the aftermath of the Coronavirus Disease 2019 (COVID-19) pandemic. Amidst this transformative landscape, artificial intelligence emerges as a vital force, addressing consumer needs by harnessing data analytics for predicting and monitoring rental prices. While studies have demonstrated the efficacy of machine learning (ML) algorithms such as decision trees and neural networks in predicting house prices, there is a lack of research specifically focused on rental property prices, a significant sector in Brazil due to the prohibitive costs associated with property acquisition. This study fills this crucial gap by delving into the intricacies of rental pricing, using data from the city of São Carlos-SP, Brazil. The research aims to analyze, model, and predict rental prices, employing an approach that incorporates diverse ML models. Through this analysis, our work showcases the potentia",1.0,https://doi.org/10.3390/stats8010012
44adc7031733c69e5a5f3bfaafc0f121ba07eed4,10.5070/bp328133859,A MODIFIED AGENT-BASED MODEL OF SLUM FORMATION,2017.0,,Alexander J. McGrath,"This paper investigates how the inclusion of political lifecycles and unrestricted housing development by private developers will impact the spatial arrangement and density of slums in a virtual urban environment. To do this, I build on the agent based model (ABM) entitled “Slumulation” developed by Crooks, Koizumi and Patel (2012). The intention of this is to generate conversation around the ways individual action impact the urban en- vironment, and also how other stakeholders in the city create conditions that motivate the emergence of certain spatial arrangements over time. Through the addition of code into the original model, I am able to augment the actions of two actors in particular: politicians and developers. Borrowing from literature, I include local political cycles that minimize the interaction between urban dwellers and politicians throughout most of the simulation, except for in the case of election times where special consideration is made that allows for lower rents and lax rule enforcement in exchange for political support. In the center of this city, housing developers are programmed to build housing for high- and middle-income households because the real estate s",1.0,https://cloudfront.escholarship.org/dist/prd/content/qt3qk1w16d/qt3qk1w16d.pdf?t=poowqa
6cef8d547d15165863844d240e936d1efa91edc7,10.37745/ijphg.13/vol10n1117,"Livability Correlates of High Rise Buildings in Eti-Osa Local Government Area, Lagos, Nigeria",2022.0,International journal of physical and human geography,Akindele O. Akin; B. Oguntola; F. Alabi; A. S. Odetoye; M. Bello,"Owing to the continuous compaction of cities and mega cities through indiscriminate proliferation of high-rise buildings, this research investigates the livability correlates of high-rise buildings in Eti-Osa. Nearest Neighbour Analysis was used to explain the spatial pattern of the high rise buildings. Chi square was used to explain the spatial variation in land uses within high-rise buildings. Independent two-sample t-test was used to explain the spatial variation in livability. Principal component variant of factor analysis was used to generate linear composites of Relative Livability Quality Index (RLQI) while Multiple Linear Regression Analysis was used to explain the relationship between the incidence of high-rise buildings and the RLQI of the study area.High-rise buildings were clustered in the area (Rn = 0.622). There is spatial variation in land use within high-rise buildings (p = 0.000). Livability is moderately high in the study area with basic facility index of 57.8%, cost of living (88.9%), property value (86.1%), sense of safety (80.0%) and neighbourhood quality (62%). There is no spatial variation in residential livability quality (T = 0.880, p = 0.400). There is a r",1.0,http://dx.doi.org/10.37745/ijphg.13/vol10n1117
e5a03620c206f16a19fddf4ed7dfe6663bafd7ec,10.1108/ijhma-11-2025-0266,Housing price dynamics in Malaysia and Singapore: evidence from an ARDL approach,2026.0,International Journal of Housing Markets and Analysis,K. Sim; Consilz Tan,"
 
 This study aims to investigate the macroeconomic determinants that influence housing prices in Malaysia and Singapore, two Southeast Asian economies with distinct policy frameworks and housing market characteristics. The research seeks to identify both short-run and long-run dynamics that shape housing market behaviour and to provide comparative insights for policymakers and investors.
 
 
 
 Quarterly time-series data from 2000 to 2024 are analysed using the Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration and the error correction model (ECM). The Granger causality test is used to determine the direction of relationships among variables. Housing price index (HPI) serves as the dependent variable, while gross domestic product (GDP), inflation (CPI), interest rate, unemployment rate and money supply (M3) act as the explanatory variables.
 
 
 
 The empirical results indicate that both economies exhibit long-run cointegration among housing prices and the selected macroeconomic variables. Money supply and GDP growth emerge as dominant long-run drivers in both countries, while inflation and interest rates show mixed short-run effects. Singapore demonst",1.0,
9ce363f5b627e69de54e30ac1f1d31c3e56f3951,10.21121/eab.1289964,Prepayment and Default Risks of Mortgage-Backed Security Collateral Pools,2023.0,Ege Academic Review,Tuğba Güneş; A. Apaydın,"Mortgage-backed securities (MBS) are structured financial products that are produced via securitization of mortgage loans. Due to the nature of securitization, all risks of mortgage loans are transferred from originators to MBS investors. Prepayment and default risks of mortgages lead to uncertainty in MBS cash flows and create a complex problem for valuation of these instruments. Therefore, estimating these mortgage termination risks has become the focus of valuation of MBS collateral pools. This study explores two questions by using a publicly open dataset provided by Fannie Mae. First, two machine learning algorithms (Random Forest and Multinomial Logit Regression) are used for classification to predict whether a mortgage loan is likely to be prepaid, defaulted or current. Afterwards, Competing Risks Cox Regression Analysis is performed to see determinants of when mortgage termination risks are likely to happen. It is found that not all mortgage borrowers behave optimally in their prepayment and default decisions. Therefore, in addition to refinancing incentive and negative equity which depend on variations in prevailing mortgage interest rates and housing prices, heterogeneity ",1.0,https://dergipark.org.tr/tr/pub/eab/issue/82667/1289964
9e00dfd089dca40befbcf5aaeda4364373e32dc3,10.22610/imbr.v16i3(i)s.4089,The Impact of Taxation on the Cost of Living: A Comprehensive Analysis,2024.0,Information Management and Business Review,S. Mainal; Fatin Alwani Abu Hanipah; Nur Liyana Zuhaimi; Nur Nuzul Syahira Kamarudin,"This study examines the taxation-related factors affecting Malaysia's cost of living. The rising cost of living, a persistent issue in economics and social well-being, is often linked to global challenges faced by emerging nations. Direct tax has been identified as a primary contributor to the financial strain on households, who must manage various essentials beyond food, clothing, and shelter, such as education, cellular services, and transportation. The study analyzes the relationship between seven independent variables—direct tax, unemployment rate, exchange rate, household consumption expenditure, house price index, subsidies, and net trade—and the cost of living in Malaysia, using 30 years of annual data (1992-2022) with EViews for econometric analysis. This study uses descriptive statistical analysis for the description of data, coefficient matrix analysis and regression analysis for determining the impact of dependent and independent variables. The findings show that the cost-of-living pressures are positively correlated with direct tax, housing price index, and net trade. Data was sourced from the National Property Information Centre (NAPIC), the World Bank, and the Economi",1.0,https://doi.org/10.22610/imbr.v16i3(i)s.4089
d692f7288ff0495dd871e357c8dd58bc9575727e,10.47743/saeb-2026-0008,Access to Housing and Economic Growth in the European Union,2026.0,Scientific Annals of Economics and Business,P. Bação; A. Conceição; Marta C. N. Simões,"The European Union is facing what some identify as a housing crisis characterized by rising housing prices and rents and a dearth of affordable housing. Beyond its concerning direct effects on household well-being, the housing crisis may also pose long-term risks to macroeconomic performance. Escalating housing prices and rents can result in resource misallocation and shortages of highly skilled labor in key places, as well as increasing inequality, which could ultimately slow growth. An empirical assessment is conducted to examine the relationship between housing affordability pressures – proxied by the share of housing expenditure in household final consumption – and economic growth in the European Union, using data from 21 member states spanning the period from 2000 to 2019. We estimate a growth regression with country and time fixed effects and alternative inference approaches, and include a comprehensive set of control variables selected from prior literature. In this macro panel, inference is sensitive to the covariance estimator; we therefore report Driscoll-Kraay and country-clustered inference and focus on the sign, magnitude, and robustness of the estimated association. U",1.0,https://saeb.feaa.uaic.ro/index.php/saeb/article/download/3641/415
c24db66393a73ad98b832c58b1a2db5738e1957b,10.1007/s41685-022-00261-8,"Special Feature on social, economic, and spatial impacts of COVID-19 pandemic in Turkey",2022.0,Asia-Pacific Journal of Regional Science,T. Baycan; Suat Tuysuz,"This Special Feature investigates the social, economic, and spatial impacts of the COVID-19 pandemic in Turkey and highlights the factors differentiating Turkey from the other countries. The articles contributing to this Special Feature are classified into three main parts. The first group of articles addresses spatial implications of the COVID-19 pandemic in Turkey with a specific focus on the place-based factors affecting the spread of the pandemic, the determinants of pandemic-induced changes in intracity mobility, and the use of social media to forecast commercial real estate figures during COVID-19. The second group of articles investigates the social and economic implications of the COVID-19 pandemic and diversely affected economic sectors in Turkey. These articles analyze the vulnerability and resilience of regions and diversely affected economic sectors with a specific focus on the housing market that displays an opposite trend to international tendencies regarding transaction volumes and private rental housing prices. The third group of articles considers the economic impact of the COVID-19 pandemic from an international perspective. These articles analyze the impact of th",1.0,https://link.springer.com/content/pdf/10.1007/s41685-022-00261-8.pdf
d29def971aa3bd1b64672ff45e725f24b52a3b6a,10.5171/2021.146557,"Comparing Residential Property Values in Border Towns of Lagos Megacity, Nigeria",2021.0,Journal of South African Business Research,O. Oloke; D. Jesusegun; Oluwole A. Alagbe; A. Akindele,"In this study, the rental values of residential properties in two adjacent communities within the emerging Lagos megacity were examined to find out if certain correlational attributes exist. The communities selected are located in different geopolitical zones (i.e. Lagos and Ogun States), although the Lagos megacity influence has crawled and absorbed parts of Ogun state, such that the physical boundary has virtually vanished. Residents in the two communities as well as estate surveyors and valuers were the respondent groups involved in the study. A total of 236 questionnaires were administered. Descriptive and inferential tools of percentage means and independent sample T-test statistics were employed for the analysis, while the Pearson coefficient of correlation was used to establish the relationship between rental values in the two communities. It was found, among others, that the level of infrastructure development, enforcement of physical planning regulations, administrative jurisdiction, population density and distance to the central business district are the factors responsible for the significant differences in the magnitude of rental values between the communities. The stud",1.0,https://ibimapublishing.com/articles/JSABR/2021/146557/146557.pdf
eb20c4e32e41208031fd85047fe11031253943d6,10.5762/kais.2016.17.3.518,Comparative Analysis of the Causal Relationship between Regions of Fluctuations in the Housing Market,2016.0,,Kyong-hoon Kim; Ho-Myun Jang,"The housing market is changing continuously according to the place and time and these changes have a ripple effect across various fields. On the other hand, the amount of housing that is consumed in the region also acts as a central cause of price movement. Moreover, the cause of variations in the housing market can be separated according to the characteristics of the housing consumer. In addition, the individual characteristics of the consumer varies according to the region. As a result, a study on the regional causal relationship of the housing market is underway. Although significant research has been done on the domestic home sales market, there has been limited research on the housing charter market. Therefore, in this paper, regional causal relationship of the housing market in the Gangnam and Gangbuk area in Seoul and Gyeonggi Province was analyzed using the vector error correction model, and is segmented by housing sale market and housing jeonse market. In addition, housing sale and housing jeonse of Gangam, Ganbuk and Gyeonggi province are defined as analysis variables, and time series data is the monthly material of June 2003 to November 2015. The results of the analysis,",1.0,https://koreascience.or.kr:80/article/JAKO201613360957201.pdf
7b5fac902299872781620948c9eb739fc8f3997b,10.52783/jisem.v10i43s.8496,The Effects of Zoning Regulations and Land Use Planning on Property Values in Sultanate of Oman,2025.0,Journal of Information Systems Engineering & Management,M. A. Al Amri,"This research seeks to analyse the impact of zoning regulations and land use planning on property values in the Sultanate of Oman. Zoning is a significant legal tool for regulating land use, building densities, and environmental standards. While zoning can boost house prices by limiting supply and curtailing the development of inappropriate land use, too rigid policies could hamper land use efficiency. This study is about how zoning affects property values by supply, amenity, scarcity, and external impacts. For example, the limitation of the population density increases prices because of the decline of the stock of housing and the increase in demand. Preserving green space and neighbourhood appeal and, thereby, property values is another area in which zoning can also be used. Real estate costs are compared across Muscat, Sohar, and Sur, all of which are characterized by different urban, industrial, and rural attributes, before examining how infrastructure development determines the zoning outcome. The objective of the study is to find out if zoning laws in three districts may decreasing real estate value. Solving these would attract investment, balance property prices, and promote ",1.0,https://jisem-journal.com/index.php/journal/article/download/8496/3861
266ac2a413267733d6680beb3c4fe26a80601b23,10.3390/urbansci10050244,Greening the City with the 3–30–300 Rule: A Spatial Justice Perspective on Housing Governance and Green Gentrification,2026.0,Urban Science,S. Aliakbari; A. Russo,"Urban forestry research increasingly promotes proximity-based benchmarks, such as the 3–30–300 rule, to expand tree canopy, enhance access to nature, and support healthier and more climate-resilient cities. However, a growing body of evidence links green proximity to rising property values and residential displacement, raising concerns regarding green gentrification. These tensions suggest that proximity-based greening cannot be understood solely as an environmental or accessibility intervention; rather, its social outcomes are mediated by the broader housing system. This Perspective argues that the 3–30–300 rule operates as a value-generating urban forestry intervention whose distributive effects are conditioned by housing governance, tenure structures, and the presence of affordability protections. We advance a governance-conditional framework that reconceptualises the rule as a housing-conditioned greening strategy, illustrating how environmental improvements may translate into escalating housing costs and displacement pressures in contexts where housing regulation is weak or fragmented. The analysis highlights the institutional mechanisms through which environmental value is ca",1.0,https://www.mdpi.com/2413-8851/10/5/244/pdf?version=1777620102
922f998abd65e29da008f8bef799d74593b84a64,10.33317/ssurj.504,House Price Prediction of Real Time Data (DHA Defence) Karachi Using Machine Learning,2022.0,Sir Syed University Research Journal of Engineering & Technology,Lata Bai Gokalani; B. Das; Dilip Kumar Ramnani; Mahender Kumar; M. Shah,"Pakistan’s real estate market has a large impact in GDP growth. Investment in real estate sector in Pakistan is encumbered with lucrative opportunities. The market demand for housing is ever increasing year by year. House sales prices keep on changing and increasing frequently, so there is a need for a system to forecast house sales prices in the future. Several factors that influence house sales price includes; location, physical attributes, number of bedrooms as well as several other economic factors. One of the main motivation of choosing Karachi for the house prediction is that Karachi is capital of Sindh and it has significant importance in country's economic as it is the major commercial and industrial center of Sindh. It is one of the main contribution of the work is that through this the house prediction model based on DHA Karachi data is developed and as per best of our knowledge till today there is no prediction of housing for the country’s important has been developed. has This research paper mainly focuses on real time Defense Housing Authority (DHA) Karachi data, applying different regression algorithms like Decision tree, Random forest and linear regression to find th",1.0,https://sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/download/504/176
ab8cf2bdd57a6477bd02fab8d181e186e42a6d32,10.1080/21650020.2025.2598533,Smart urban forecasting: AdvancedNet's AI-driven approach to housing price prediction in prefecture-level cities,2025.0,"Urban, Planning and Transport Research",Fan Wu; T. Kee,"ABSTRACT Sustainable urban development requires reliable prediction tools to understand housing market dynamics. This study develops and evaluates AdvancedNet, a deep learning model designed to capture nonlinear relationships in a comprehensive urban panel dataset covering 290 Chinese prefecture-level cities from 2011 to 2021. The model integrates the house price index (HPI) with macroeconomic, demographic, employment, and infrastructure indicators, drawing on recent advances in machine learning for modeling complex urban systems. AdvancedNet incorporates batch normalization, dropout, L2 regularization, and Box–Cox transformation to improve generalization and stability when handling high-dimensional data. Empirical results show that AdvancedNet achieves an out-of-sample R² of 0.96 and reduces the RMSE by approximately 20% compared with benchmark econometric and machine learning models, demonstrating superior predictive performance. It also captures the evolution of HPI rankings among major cities, highlighting significant divergences between rapidly developing coastal regions and slower-growing inland areas. This study contributes by constructing a unified multi-source urban datase",1.0,https://www.tandfonline.com/doi/pdf/10.1080/21650020.2025.2598533?needAccess=true
c5e0a5be265cc8d68457911db73f2d996b1cdb5d,10.1080/1331677x.2023.2179505,Estimating the effect of long-term mechanism for real estate in China: a regression discontinuity approach,2023.0,Economic Research-Ekonomska Istraživanja,Shi Zheng,"Abstract Housing prices in China have risen dramatically in recent decades, giving rise to bubble concerns. Many studies show that short-term regulation of real estate market is ineffective, which drove central government to propose the construction of a long-term mechanism for real estate in late 2016. However, few studies have been conducted to estimate its effects. This article highlights the effects of the long-term mechanism on housing prices, similar to a quasi-natural experiment, with the proposal of the long-term mechanism. In this context, the study was conducted with the end of 2016 as a breakpoint, using the RDiT method and panel data for 35 large- and medium-sized cities from 2009 to 2021, to find out whether the long-term mechanism has stabilized housing prices or not. The results reveal that the long-term mechanism dramatically reduced the divergence and dispersion of housing prices from the long-term equilibrium level. Further study shows that there is significant heterogeneity in the impact of the long-term mechanism on housing prices across cities, with significant effects in second-tier cities but not in first-tier cities, and with significant effects in central a",1.0,https://www.tandfonline.com/doi/epdf/10.1080/1331677X.2023.2179505?needAccess=true&role=button
a7d7d518e27311e61bc9a8dde2eab286b81e3cde,10.3390/ijfs14050130,Development of a Predictive Tool for Real Estate Analysis Using Machine Learning Techniques,2026.0,International Journal of Financial Studies,Ricardo Francisco Reier Forradellas; Gregorio Acedo Benítez,"The real estate market is a complex and dynamic sector that plays a key role in economic stability and wealth generation. In many regions, real estate assets represent around 80% of household wealth, while rising housing prices have turned access to housing into a major social and economic challenge. In this context, the availability of accurate and accessible information is essential for decision-making by buyers, investors, and public administrations. This study proposes the development of an advanced technological tool based on Artificial Intelligence and Machine Learning techniques to predict and analyze real estate market dynamics within a specific geographic area. Using the city of Madrid as a case study, the research presents a digital application capable of estimating the market value of a property by analyzing comparable recently sold properties and incorporating key housing characteristics. By entering an address and a set of property features, the system generates a precise and data-driven valuation. The results demonstrate that AI-based approaches can significantly improve the accuracy and accessibility of real estate valuation processes. The proposed methodology enable",1.0,https://doi.org/10.3390/ijfs14050130
1f07d92a2d64f3e61dbfc427448b78696064e391,10.22335/rlct.v12i2.1165,"Incidencia del delito en los cánones de arrendamiento en la Loma de Los Bernal, Medellín - 2017",2020.0,"Revista Logos, Ciencia & Tecnología",Oscar Alonso Ospina Espinosa; Diego Fernando Martínez Montoya; Jorge Enrique Agudelo Torres,"The incidence of crime in home rentals has not traditionally been analyzed in depth in Colombia due, among other factors, to the low availability of information related to the real estate market that exists as a consequence of the low perception of security, which makes it difficult for this type of data to be available to researchers, since it is continuously kept under reserve. The main objective of this study is to establish a functional relationship between the housing rentals in the Loma de Los Bernal neighborhood in Medellín, and crimes such as theft of motorcycles and personal injuries. In addition, it also offers percentage estimates of the reduction in the value of home rentals as a result of the commission of these crimes. The technique used was that of geographically weighted regressions. With this procedure it is possible to obtain results with higher levels of adjustment when compared with other methodologies, thanks to the fact that it allows to spatially weight all of the data obtained from the Real Estate Market of Medellín and Antioquia, as well as from the portal datosabiertos.gov.co. The main revelation of this work includes finding mathematical evidence that per",1.0,https://revistalogos.policia.edu.co:8443/index.php/rlct/article/download/1165/1502
f8f27f637e2ef66e57a8f1668d3ca1b9bfc85cb0,10.1007/s41060-026-01204-8,"Multimodal machine learning in real estate appraisal: models, data, and trends",2026.0,International Journal of Data Science and Analysis,Chen-Ya Huang; Bin Liang; Zhidong Li; Justin Wang; Fang Chen,"Real estate appraisal has undergone a significant transition from manual to automated valuation and is entering a new phase of evolution. Leveraging comprehensive attention to various data sources, a novel approach to automated valuation, multimodal machine learning, has taken shape. This approach integrates multimodal data to deeply explore the diverse factors influencing housing prices. Furthermore, multimodal machine learning significantly outperforms single-modality or fewer-modality approaches in terms of prediction accuracy, with enhanced interpretability. However, systematic and comprehensive survey work on the application in the real estate domain is still lacking. In this survey, we aim to bridge this gap by reviewing the research efforts. We begin by reviewing the background of real estate appraisal and propose two research questions from the perspective of performance and fusion aimed at improving the accuracy of appraisal results. Subsequently, we explain the concept of multimodal machine learning and provide a comprehensive classification and definition of modalities used in real estate appraisal for the first time. To ensure clarity, we explore works related to data a",1.0,https://link.springer.com/content/pdf/10.1007/s41060-026-01204-8.pdf
b266e62fe8f2fc1666440419c3fe4180a98d2707,10.1108/jdqs-05-2022-0013,A comparative study on determinants of housing mortgage prepayment of individual borrowers,2022.0,Journal of Derivatives and Quantitative Studies,Sang Won Lee; S. Ryu; Tae Young Kim; Jin Q. Jeon,"This paper examines how the macroeconomic environment affects the determinants of prepayment of mortgage loans from October 2004 to February 2020. For more accurate analysis, the authors define the timing of prepayment not only before the loan maturity but also at the time when 50% or more of the loan principal is repaid. The results show that, during the global financial crisis as well as the recent period of low interest rates, macroeconomic variables such as interest rate spreads and housing prices have a different effect compared to the normal situation. Also, significant explanatory variables, such as debt to income (DTI) ratio, loan amount ratio and poor credit score, have different effects depending on the macroenvironment. On the other hand, in all periods, the possibility of prepayment increases as comprehensive loan to value (CLTV) increases, and the younger the age, the shorter the loan maturity. The results suggest that, in the case of ultralong (40 years) mortgage loans recently introduced to support young people purchasing houses, the prepayment risk can be, at least partially, migrated by offsetting the increase in prepayment by young people and the decrease in prepa",1.0,https://www.emerald.com/insight/content/doi/10.1108/JDQS-05-2022-0013/full/pdf?title=a-comparative-study-on-determinants-of-housing-mortgage-prepayment-of-individual-borrowers
8d6f40adf73664f3999206f22cf1924a12dcd267,10.54254/2754-1169/69/20231439,The Impact of Spatial and Locational Characteristics on Airbnb Prices in London,2024.0,"Advances in Economics, Management and Political Sciences",Xinran Liu; Yuxuan She; Meihan Liu,"This research delves into the intricate relationship between spatial and locational attributes and Airbnb pricing in London. Utilizing data encompassing Airbnb listings in London collected over one year concluding on December 10, 2022, and employing advanced geospatial statistical techniques, including a geographically weighted regression (GWR) model equipped with ten select explanatory variables, this study reveals multifaceted spatial patterns underlying Airbnb pricing. The study underscores the paramount significance of dissecting the multifaceted determinants of Airbnb pricing, encompassing property characteristics, location-specific variables, host-related attributes, and customer feedback. Through empirical analyses, this research illuminates pronounced spatial heterogeneity within Airbnb pricing, with notable variations discerned across different room types. Interpretation of model coefficients reveals the multifaceted influence of factors, such as proximity to subway stations, volume of customer reviews, and specific scores, on pricing dynamics. Additionally, the GWR model exposes significant spatial variations in the impact of location and neighborhood-related variables on",1.0,https://www.ewadirect.com/proceedings/aemps/article/view/9504/pdf
6d1f9334c7b1e9a5c3083aceb02085828371661a,10.2991/iserss-18.2018.43,Analysis of Factors Affecting the Housing Price in Different Cities of China and Macro Control Measures,2018.0,,Xuejie Ding; Ran Huang; Xue Wang,"In response to the rapidly rising of house prices in major cities across the country, the central and local governments at all levels have implemented multi-round home buying restriction policies since the end of 2016. Although the effectiveness of these policies is obvious at present, their impacts are lack of sustainability, and they are not conducive to the longterm development of the real estate market. Only by the in-depth analysis of the root causes of rising house prices in different types of cities and the macro-control policies of developing differentiation, can the real estate market in China achieves sustainable development. Based on the classical supply-demand theory, this paper studies the main influencing factors of house prices in the first, second and third tier cities by using gray relational analysis and principal component regression analysis. The results show: housing prices in the first-tier cities is a typical demand-pull housing prices; Second-tier cities housing prices is mainly affected by demand factors, but the influences of some supply factors are gradually increasing at the same time; Thirdtier cities housing prices is simultaneously affected by a numbe",1.0,https://download.atlantis-press.com/article/25899488.pdf
ca4682f093e926cb942c787275fd09ab9265d92b,10.22313/reik.2024.22.1.15,Well-being Housing Service Business Model Considering the Characteristics of Each Type of Elderly Household,2024.0,Residential Environment Institute Of Korea,Yongkyung Cho; Jaehwan Kim; YoungHo Yoon,"Korea is rapidly aging, with the elderly population accounting for approximately 18% of the total population, entering an aging society, and is expected to soon enter a super-aged society. To understand the characteristics of the elderly, a cohort approach is necessary. Accordingly, the purpose of this study is to find out through analysis of variance (ANOVA) whether there are differences between groups in terms of economics and life by age group, based on the baby boom generation, a representative cohort group in Korea, and to identify types within the elderly, not as a single group. It's in doing. In addition, the purpose is to categorize the housing services required for each type of senior citizen and present them as a commercialization model. The results of the ANOVA showed that there were differences between groups in most variables, and the housing prices, rental deposits, and earned income of the elderly were also found to decrease. Social insurance by age group remains higher than that of the previous generation for those reporting an age, and in particular, the social insurance benefit rate has increased. Meanwhile, existing seniors and new seniors were found to have diff",1.0,
75a6485daf6c288200fc332c28b7abb1bebc0f01,10.12962/j2716179x.v13i1.5226,Structural Equation Model For The Relationship between Property Value and Public Transport Accessibility,2018.0,Jurnal Penataan Ruang,S. Nurlaela,"Property value capitalization has been part of spatial-economic results of transportation infrastructure development. This paper presents a study to understand the relationship between property value and public transport accessibility. A structural equation model or SEM in terms of path analysis was developed and explained the backward-forward chain of sequential causality of property value, public transport accessibility and travel behavior. The backward chain explains the transportation influences land use in a sequential causality and the forward chain explains the relationship from land use to travel behavior. Path analysis reported the backward chain was only valid in a direct relationship from public transport accessibility to property value but failed to account for a sequential causality relationship from public transport accessibility, land use density/intensity and property value. The forward chain confirmed the significant relationship from accessibility to travel time and to the number of household trips through the influence of land use, however at low influence. Direct relationship from accessibility to travel behavior (car uses) mediating by travel distance was at mo",1.0,https://doi.org/10.12962/j2716179x.v13i1.7062
d964cf81c48bf4d67240575c62dbb4fcb05dae4c,10.54254/2755-2721/2025.20931,Review of Housing Price Forecasting Methods Based on Machine Learning and Deep Learning,2025.0,Applied and Computational Engineering,Chenyu Li,"Housing price forecasting holds profound significance in the development, investment, and policy formulation within the real estate market. Precise forecasts not only assist real estate developers in evaluating the potential value of projects but also furnish investors with a scientific basis to optimize their investment decisions. At the same time, home buyers can also gauge the price trend and appreciation potential of properties based on the forecast results. Moreover, governments and financial institutions rely on reliable housing price forecast results to formulate reasonable market supervision policies and control loan risks. This article discusses the status of house price forecasting in society in recent years and the latest research progress in applying machine learning and deep learning. Notably, it zeroes in on the role that the fusion of numerical features, textual descriptions, and image features plays in predicting housing prices. Through a methodical systematic of the existing research, this paper analyzes the characteristics, advantages and disadvantages of different models and their application scenarios. The results show that the fusion of multi-source data can si",1.0,https://ace.ewapub.com/article/view/20931.pdf
c1c06879bbd671d63e05c737e134d01c273b3e2b,10.11648/j.sd.20200803.12,Linear Regression Model of House Price in Boston,2020.0,,Ying Niu,"The change of house price is a common phenomenon. People are eager to grasp the law of house price to become the winner of real estate investment. This paper uses Boston house price data to explore the relationship between Boston house price and which independent variables. This paper uses linear regression model to construct the relationship between housing prices and crime rate in Boston. First, the classical linear model is adopted. Then we do the collinearity test, removing the lever point and other operations, the residual of the model still does not conform to the normal distribution, so the classical linear model cannot describe the data very well. Then, we add the quadratic term and the cross term, and use the method of stepwise regression to get the optimal regression autoregressive quantum set. After removing the leverage point and significance test, we found that the residual distribution was approximately normal. It shows that the improved model has well described the law of data. Finally, according to the data, the main conclusions are as follows: house price and tax rate, index close to the highway and index close to the city center are inversely correlated, which is ",1.0,https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20200803.12.pdf
2ba4dc4628fc2c9cf04c3ffb150a87f3bc52eb18,10.17509/seict.v6i1.86768,Review of Banjarnese Neural Machine Translation Development With Minimal Resources,2025.0,"Journal of Software Engineering, Information and Communication Technology (SEICT)",Ali Muhammad; Novia Winda; A. Firizkiansah; Dita Setiawan; Siti Herawati Fransiska Dewi; Imron Rizki Maulana; Miri Ardiansyah,"With the advancement of information technology, the application of machine learning in the property industry, particularly for house price prediction, has become increasingly important. Technology plays a crucial role in speeding up and enhancing the accuracy of property buying and selling processes. Therefore, the role of machine learning technology can be utilized to meet the need for improving the accuracy of house price predictions in major cities of developing countries, such as Bandung. This research aims to analyze the effectiveness of the Artificial Neural Network and Random Forest algorithms in predicting house prices in Bandung. The data used includes house sales data in Bandung, covering land area, building area, number of bedrooms, number of bathrooms, number of parking spaces, and the subdistrict location. The analysis of the algorithms is conducted by comparing the performance testing results of both algorithms using performance metrics for regression models such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-Square (R2). Additionally, this research analyzes which data ratio among the training, validation, and test data y",1.0,https://ejournal.upi.edu/index.php/SEICT/article/download/86768/33634
d1c948d264dac23935c09f10d88dd082602f66c9,10.54254/2753-8818/42/2024ch0213,Factors affected housing prices: taking Boston as an example,2024.0,Theoretical and Natural Science,Ao Wang,"Abstract. Real estate price prediction plays a vital role in urban planning, investment decision-making, and risk management. However, existing prediction models often show problems such as insufficient generalization ability and susceptibility to outliers when faced with complex nonlinear relationships, multidimensional features, and noisy data. Therefore, choosing a model that can accurately capture complex patterns and has strong robustness has become the focus of research. This paper introduces the random forest model and compares it with multivariate linear regression, XGBoost, and support vector machine (SVM). Compared with the traditional regression model, the random forest model combines the flexibility of decision trees and the multi-level feature extraction ability of deep learning, and can better handle the complex nonlinear relationships in the Boston housing price dataset. The experimental results show that the random forest model has achieved excellent performance in all evaluation indicators, and the model accuracy indicators are distributed as MSE=8.2502, RMSE=2.8723, MAE=2.0668, and R^2=0.8875. These results show that the random forest model not only outperforms ot",1.0,https://www.ewadirect.com/proceedings/tns/article/view/16420/pdf
3efaf30e3fdc005ea2b4a3512399b6dc6974264f,10.5539/ijef.v9n10p189,The Impact of Individual Characteristics and Branch Characteristics on Housing Agent Performance: Heckit model and Hierarchical Linear Modeling,2017.0,,Chun-Chang Lee,"This study investigates the impacts of individual characteristics and branch characteristics on housing agent performance. Data were analyzed using hierarchical linear modeling (HLM) to provide estimations. The empirical results suggest that individual performance varies significantly from branch to branch and is better in branches with higher levels of compensation for individual performance. Individual characteristics including college level education, having children over the age of six, work experience, the square of work experience, and work experience outside the real estate industry have significant effects on individual performance. Individual performance is also better in branches with requirements for hours worked. The individual performance of salespeople working under team compensation schemes is not significantly better than that of salespeople working in branches without team compensation schemes. When the average housing prices for the areas in which branches operate are higher, individual performance will be higher. As the average housing price for an area increases, however, the corresponding increase in individual performance will be less and less strong. Accordin",1.0,https://doi.org/10.5539/ijef.v9n10p189
d92e10e081a1dbbddb9aa9999e09c3862dd93840,10.1108/jerer-07-2020-0044,The Mortensen-Pissarides model and the empirical facts of housing markets,2021.0,,G. Lisi,"
Purpose
This paper aims to explain the main empirical facts of housing markets, notably the trade-off between housing price and time-on-the-market, the positive correlation between housing price and the number of contracts traded during a given period (i.e. the trading volume) and the existence of price dispersion.


Design/methodology/approach
This theoretical paper makes use of a search and matching model. Search and matching, indeed, are two fundamental characteristics of the trading process in the housing market, and, thus, the search-and-matching models have become the new economic approach to the analysis of real estate markets.


Findings
This paper shows that a slightly modified version of the baseline search and matching model à la Mortensen-Pissarides can explain the main empirical facts of housing markets. There are two key mechanisms that allow to achieve this notable goal: a simple formalisation of the (reasonable) assumption that buyers today are potential sellers tomorrow (and vice versa); and the direct relationship between market tightness and house price, derived by the standard matching model and underestimated by the related literature.


Research limitations/i",1.0,
cfde6915517f8fd980b0367c6f9dbca5f7baa860,10.4236/me.2022.133019,Social Utilities versus House Prices as Scales of Residential Preferences for Homes’ Attributes 30 Years Apart,2022.0,Modern Economy,A. Phipps,"One of two scales of residential preferences in a resident’s mind is their social and environmental utilities for homes’ attributes’ levels. The other is their willingness to pay for these attributes’ levels as it conforms with prices in the local real estate market. Convergence between two scales of preferences has one mentally superseding the other as primary. Divergence will more likely produce a consumption disequilibrium as the gap in price between affordable socially and monetarily most preferred attributes’ levels of homes. Convergences and divergences between these two fluctuating scales of preferences are measured with three interrelated datasets for up to 74 respondents and 3,000 single-detached(-like) houses in each of Saskatoon SK in 1987 and Windsor ON in 2020. Results are that respondents have diverging monetized and social utilities for up to seven of 12 generic attributes of homes in 1987 and/or 2020. They therefore will frequently have large consumption disequilibria for these attributes. The maximum of these will average up to one-half more than the price of an attribute’s socially most preferred level if they want its monetarily most preferred level. Despite this",1.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=116007
60d9a8ae5a49f5b5eac9723b50a073f1ef33fac0,10.32599/apjb.11.4.202012.189,The Dynamic Relationship between Household Loans of Depository Institutions and Housing Prices after the Financial Crisis,2020.0,The Institute of Management and Economy Research,Gyu-Sik Han,"Purpose - This study aims in analyzing the dynamic relationship between household loans and housing prices according to the characteristics of depository institutions after the financial crisis, identifying the recent trends between them, and making policy suggestions for stabilizing house prices. 
Design/methodology/approach - The monthly data used in this study are household loans, household loan interest rates, and housing prices ranging from January 2012 to May 2020, and came from ECOS of the Bank of Korea and Liiv-on of Kookmin Bank. This study used vector auto-regression, generalized impulse response function, and forecast error variance decomposition with the data so as to yield analysis results. 
Findings - The analysis of this study no more shows that the household loan interest rates in both deposit banks and non-bank deposit institutions had statistically significant effects on housing prices. Also, unlike the previous studies, there was statistically significant bi-directional causality between housing prices and household loans in neither deposit banks nor non-bank deposit institutions. Rather, it was found that there is a unidirectional causality from housing prices t",1.0,
65d9687d64ddb49df322bdfad1d6c80e6ad36636,10.3390/urbansci10060315,Explainable AI for Urban Real-Estate Prediction: A Machine-Learning Framework for Urban Decision Support,2026.0,Urban Science,V. Saiu; Matteo Mocci,"This study introduces RE-VAL (REal-estate VALuation), an explainable framework for urban real-estate analysis that integrates reproducible data acquisition, geographically informed feature processing, predictive benchmarking, and interpretable outputs suitable for decision-support-oriented analysis. Unlike static automated valuation models, the RE-VAL framework is designed to reflect context-dependent market behaviour across heterogeneous urban areas. The comparative evaluation on 1153 residential listings from Cagliari (Italy) showed that MLP achieved the strongest predictive performance, while Random Forest provided the most convincing balance between predictive competitiveness and interpretability. Beyond point estimation, the framework leverages SHAP-based decomposition to translate algorithmic outputs into transparent, monetary-based “Bonus/Malus” adjustment tables. The analysis highlights the presence of potentially non-linear interactions, including a possible premium associated with energy efficiency in prestigious areas, and suggests that the framework can remain informative when incomplete technical data are preserved as potential proxy signals rather than being discarded",1.0,https://doi.org/10.3390/urbansci10060315
4f6fa8477e0f0d6c532fb2bf49e6f7a11e0860f0,10.3390/realestate3030008,"Predicting a Housing Price Index: A Two-Stage Machine Learning Approach Using Linked Micro-, Socio- and Macroeconomic Data from Frankfurt am Main",2026.0,Real Estate,J. Schmid,"This study develops and evaluates a two-stage machine learning framework for forecasting the condominium price index of pre-pandemic market data of Frankfurt am Main, Germany, one quarter ahead. To the best of the author’s knowledge, it is the first study to combine German micro-level transaction and listing data, socioeconomic variables and macro-financial indicators in a single residential price-forecasting framework. Furthermore, it provides the first evidence on machine learning-based transaction price index forecasting in Germany. Methodologically, the framework links disaggregated and aggregate forecasting. In stage 1, prices per square metre are estimated for four market segments using ordinary least squares, random forest, extreme gradient boosting, and a stacked ensemble in a strictly out-of-sample expanding-window design. In stage 2, these predictions are combined with lagged index values and macro-financial indicators to forecast the city-wide index. The stage 2 model achieves a relative root mean squared error of 2.25% and a mean absolute percentage error of 1.85%, outperforming a naïve persistence benchmark by reducing root mean squared error by 23%. Model interpretati",1.0,https://doi.org/10.3390/realestate3030008
cc26367596a08763be0f136ae2db47ce6d6bcf14,10.47688/rdp2023-01,The Effect of Credit Constraints on Housing Prices: (Further) Evidence from a Survey Experiment,2023.0,RBA Research Discussion Papers,Tom Cusbert,"The response of housing prices to financing conditions is determined by the effect on the marginal buyer, not the average household. I use heterogeneous willingness to pay (WTP) data from a stated preference experiment in Fuster and Zafar (2021) to estimate the effects of changes in mortgage rates and collateral constraints on housing prices by analysing the structure of housing demand curves. This work builds on their research, which focused on average changes in WTP. Relaxing down payment constraints has a large average effect on WTP, but the effect on price is less than half as large. Financially constrained households tend to respond more to relaxed constraints, but those households often have WTPs that are too low to affect market prices. Changing the mortgage rate has the same average effect on WTPs and on market prices, because there is no systematic relationship between a household's response to mortgage rates and their location on the demand curve. I use a heterogeneous user cost model of individual WTPs to understand how household heterogeneity determines the structure of overall housing demand. An empirical model using observable household characteristics allows the expe",1.0,https://www.rba.gov.au/publications/rdp/2023/pdf/rdp2023-01.pdf
3e22e2e4200a385586271d0056806ec1a9d0d607,10.5430/ijfr.v6n4p22,Testing the Present Value Model and Domino (Ripple) Effects for Hong Kong Private Residential Housing Prices,2015.0,,Kwok-Chiu Lam,"Under the assumptions of perfect market, house prices should theoretically conform to the present value model (PVM), with expected future rents and discount rates being the two determinants. The objectives of this paper are twofold. First, to evaluate the PVM for Hong Kong private residential housing prices on submarket and territory-wide basis, under the premises of constant and nonconstant homeowner costs of capital. Following Meese and Wallace (1994) approach, we find that the PVM holds in the long run, though temporary deviations are likely. The Campbell and Shiller (1987) approach, on the contrary, generally supports the PVM. This paper would probably be the first examination to that end using Hong Kong empirical data. Second, Ho, Ma and Haurin (2008) find apparent uni-directional Granger-causal relationships (domino effects) along the quality tiers. We will explore not only such effects further on intraarea basis using more recent data, but also the intraclass ripple effects among the three geographical areas (Hong Kong Island, Kowloon, New Territories). We find that domino effects exist only marginally in the New Territories. On the other hand, the ripple effects do exhibit ",1.0,http://www.sciedupress.com/journal/index.php/ijfr/article/download/7742/4626
94366bb23458480f07b64e5b6f9cbf042da33b71,10.54097/z336qy32,Study of Influential Factors on Housing Prices by Using MLR,2024.0,Highlights in Science Engineering and Technology,Chang Liu,"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, as",1.0,http://dx.doi.org/10.54097/z336qy32
d73122f18f2fc6e88c58885e906c13d19dcd95a0,10.1080/02673037.2026.2638389,Blockchains in housing markets: a simulation analysis of the impact on house price dynamics,2026.0,Housing Studies,Anetta Proskurovska; Jaroslaw Morawski,"Abstract The introduction of blockchain to real estate transactions is changing how people trade and finance property. One such shift is exemplified by tokenization, the process of recording a digital representation of traditional assets on a blockchain network. Tokenization is expected to facilitate the trading of real-world assets, making it possible to trade them 24/7, settle instantly, and do so at virtually no cost. However, the effects of nationwide adoption of blockchain technology on housing remain unclear. We build an agent-based model calibrated for the Swedish housing market to study how redesigning of the house sale process with blockchain can affect housing market price dynamics. First, we analyze the traditional conveyancing setup. Second, we simulate a streamlined transactional workflow orchestrated by a smart contract and compare it with the traditional process. The results indicate that, with all other variables being equal, using smart contracts for conveyancing leads to significantly increased transaction activity and heightened price volatility. Such changes would bestow housing markets with markedly different price and liquidity characteristics, substantially a",1.0,https://www.tandfonline.com/doi/pdf/10.1080/02673037.2026.2638389?needAccess=true
db31754132ba4c08b18fbe8b322189a07da4ef46,10.30865/jurikom.v12i6.9369,"Prediksi Harga Rumah Menggunakan Algoritma Regresi Linier,Random Forest, Dan Gradient Boosting",2025.0,JURIKOM (Jurnal Riset Komputer),Akhmadi Akhmadi; Fikri Budiman,"House price prediction is a crucial issue in the property sector because it is influenced by various interrelated factors, such as building characteristics and environmental conditions. Accurate prediction using conventional approaches is often difficult and can lead to errors in decision-making. Therefore, this study aims to develop and compare the performance of house price prediction models using three machine learning algorithms: Linear Regression, Random Forest, and Gradient Boosting. The dataset used is the Home Value Insights Dataset on Kaggle, which consists of 1,000 houses with eight main attributes. The research stages include data pre-processing, dividing training and test data, model training, parameter optimization using GridSearchCV, and performance evaluation based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) metrics using the 10-Fold Cross Validation method. The test results show that Linear Regression provides the best performance with an R² value of 0.8539 and a lower prediction error rate than Random Forest and Gradient Boosting. Although the ensemble model shows competitive results, increasing model complexi",1.0,https://ejurnal.stmik-budidarma.ac.id/index.php/jurikom/article/download/9369/4297
dcb1dc3b71d03383e58ad01d05df54f38cae5512,10.1108/sef-04-2021-0149,Floridian bankruptcies and local property damage in the United States,2022.0,Studies in Economics and Finance,B. Brotman; Brett Katzman,"
Purpose
This study aims to examine the linkage between bankruptcy filings and hurricane events. Several independent variables related to local district court bankruptcy filings are examined. The primary question posed is whether Category 3,4 and 5 hurricanes result in personal bankruptcy filings due to the real property and other damage that ensures.


Design/methodology/approach
Landfall hurricanes in Florida from 2001 through 2018 were examined by using the fully modified least square regression model. Descriptive statistics include elasticity measures that show statistics prior and post the passage of the Bankruptcy Abuse and Prevent and Consumer Protection Act of 2005 (BAPCPA).


Findings
The elasticity of housing prices was a useful statistic in explaining bankruptcy filings. Regression results indicate that bankruptcy filing occur within one year of a serious hurricane. The regression model found hurricane events and housing price trends were significant variable when predicting district court bankruptcy filings.


Practical implications
BAPCPA targets fraud under Chapter 7 bankruptcy filings. Unfortunately, this also had the unintended consequence of discouraging legitimate",1.0,
ca6eb93d8cad004dfb1dbfaf872cd1d40b593b79,10.25229/beta.1779525,Examining the Effect of Specific Macroeconomic Determinants on Housing Prices Using the ARDL Bound Test Approach: The Case of the TR3 Region,2026.0,Bulletin of Economic Theory and Analysis,M. Ersen,"The housing price index is one of the key economic indicators reflecting changes in housing prices in a country or region. This index is of critical importance, particularly for investors and financial institutions, in making short- and long-term investment decisions. Various macroeconomic variables affect the housing price index. Therefore, examining the effects of macroeconomic factors on the housing price index is of great importance. In this study, using data from the period 2020:4–2024:12, the effects of macroeconomic variables such as the consumer price index (CPI) and the industrial production index (IPI) on the TR3 region housing price index (HPI3) were analyzed using the ARDL bounds test method. According to the long-term analysis results, the CPI variable has a negative effect on HPI3 at a 10% significance level, and a 1% increase in CPI reduces HPI3 by 0.49%. The IPI was found to have a positive effect on the HPI3 at the 5% significance level, with a 1% increase in the IPI increasing the HPI3 by 7.98%. The error correction coefficient was calculated as -0.17, indicating that short-term imbalances will return to long-term equilibrium within approximately 6 months. In the ",1.0,https://dergipark.org.tr/en/download/article-file/5220977
5fed3d04e25387ad452d124dc200ed351b9a1525,10.1177/03611981251318344,"Driver’s Licenses for Undocumented Immigrants and Bus Ridership in Orange County, California",2025.0,Transportation Research Record,F. Khatun; J. Saphores,"We investigate the impact of California Assembly Bill 60 (AB 60) that allows undocumented immigrants to obtain a California driver’s license on the ridership of buses operated by the Orange County Transportation Authority (OCTA). OCTA bus ridership has fallen every year since 2012. Between 2012 and 2016, it dropped by 19% despite the launch of OCTA Bravo! in 2013 and OC Bus 360 in 2015. Changing socioeconomic conditions, poor connectivity, poor service quality, and increased competition from transport network companies are possible reasons behind this negative trend. Another potential cause is the implementation in 2015 of AB 60. In this context, this article examines the association between changes in OCTA bus ridership and the inception of AB 60 while controlling for differences in transit supply, socioeconomic variables, gas prices, multi-family rent, and single-family home value. To explain changes in monthly average weekday ridership, we estimated four route-level fixed-effect panel regression models. We analyzed ridership data for 2014 (just before the implementation of AB 60) and 2015–2016 (the first two years after the enactment of AB 60) for local, community, express, and ",1.0,https://doi.org/10.1177/03611981251318344
5cd98ed2ae6029f43c034327d3bd0ff5fdefc976,10.1371/journal.pone.0332439,An improved artificial gorilla troops optimizer for BP neural network-based housing price prediction,2025.0,PLoS ONE,Yulin Li,"In the context of global economic austerity in the post epidemic era, housing, as one of the basic human needs, has become particularly important for accurate prediction of house prices. BP neural network is widely used in prediction tasks, but their performance is easily affected by weights and biases, and thus metaheuristic algorithms are needed to optimize the network parameters. Firstly, to address the shortcomings of the artificial Gorilla Troops Optimizer (GTO) in such optimization tasks, such as reduced population diversity, easy to fall into local optimal solutions and slow convergence, this paper proposes a fitness allocation strategy, a Cauchy variation strategy, and an elite evolution mechanism to improve the algorithm, which in turn results in an improved artificial Gorilla Troops Optimizer (IGTO). Subsequently, a BP neural network house price prediction model based on IGTO is constructed and experiments are conducted on four datasets, namely, Boston, California-Bay, California-Land and Taiwan. The experiments are first compared with eleven other swarm intelligence algorithms and then with four machine learning models, and the results show that IGTO-BPNN improved 17.66%",1.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0332439&type=printable
9997b189d1b1db09bcd6685170f001fc37c02a66,10.54254/2753-8818/52/2024ch0110,Research on the influencing factors of housing price based on multiple linear regression and random forest,2024.0,Theoretical and Natural Science,Xin-Ran Jiang; Chang-Rong Li,"This paper aims to use multiple linear regression model and random forest models to analyze and study the factors affecting the housing price in Boston. The multiple linear regression model describes the relationship between multiple independent variables and one dependent variable through linear equations, and the random forest improves the accuracy and robustness by constructing multiple decision trees and combining their prediction results. To deal with complex nonlinear relationships and high dimensional data. Housing price is an important index to reflect the level and condition of economic and social development of a region, so it is of theoretical value and practical significance to explore its influencing factors and ways and degrees. Multiple factors are selected to analyze the weight and importance of each influencing factor, so as to help the government and decision makers to formulate more accurate policies, promote the stable development of the market, and provide scientific decision-making support for real estate developers, investors and ordinary buyers. In this study, the random forest model based on decision tree was used to clean, select and reduce the acquired ho",1.0,https://tns.ewapub.com/article/view/15392.pdf
db0a6726dbecbc88e20dee9cd8141be0bea2bd0d,10.30880/ijscet.2022.13.02.009,Prediction Supply for High-Cost Multi-Storey House Toward The Development of Sustainable Cities,2022.0,International Journal of Sustainable Construction Engineering Technology,Fazilah Ramli; R. Zainal; R. Kasim,"The absence of concerns about the problem of housing overhang in developer’s inventory can result in the growth rate of supply exceeding the growth rate of demand. The uncertainty of the economic sector at the international level has a direct impact on the economy in Malaysia, especially the real estate sector. This discussion paper applies literature review, analysis as well as regression approach to develop the supply prediction model for high-cost multi-storey houses. The published documents such as GDP per capita, inflation rate, unemployment rate and per centage of housing overhang for 10 years from beginning 2010 until 2020 are analyzed using secondary data analysis before regressed. Based on such findings, the GDP factor resulted in an increase in the percentage of house price supply for categories between RM 300,000 to RM 400,000, RM 600,000 to RM 700,000 and RM 800,000 to RM 900,000. Second, the factor of unemployment rate resulted in an increase in the percentage of house price supply for a category between RM 300,000 to RM400,000. Third, the factor of inflation rate resultsin an increase in the percentage of house price supplyfor categories between RM 400,000 to RM 500,0",1.0,https://publisher.uthm.edu.my/ojs/index.php/IJSCET/article/download/11301/4948
3d413d83eb0d87bcfc7e3b07ba405ac9b516db48,10.1088/1757-899x/780/7/072043,Research on residential differentiation in urban fringe areas from the perspective of spatial justice – A Case Study of Xi’an City,2020.0,IOP Conference Series: Materials Science and Engineering,Xiao-Jie Yang; Xiuyuan Li; Ziqi Kong; L. Lei,"Residence is one of the four functions of the city. The spatial layout of the residential area affects the sustainable development of the city. With the rapid development of social economy and the continuous expansion of urban space, the living space structure in the marginal area of Xi’an has been adjusted, and the living space differentiation has become increasingly obvious. Based on the theory of space justice and using the methods of space analysis, statistical analysis and field research, this paper concludes that the core of living space in the edge area of Xi’an lies in the city center, and the future development direction is northeast and southwest. The characteristics of housing price are that the middle price of residential area is the most and the number of high-end and low-end residential area is relatively small. The housing types are divided into four categories: high, high-medium, medium and low. The marginalization of residential areas is high, the middle and high-end residential areas are multi-centralized, and the middle and low-priced residential areas are marginalized, and the degree of regional differentiation is gradually deepening. This is an important featur",1.0,https://iopscience.iop.org/article/10.1088/1757-899X/780/7/072043/pdf
8d96da3e3efe6fbafc93136db16409200b281ee6,10.3390/su17177641,"Assessing Urban Safety Perception Through Street View Imagery and Transfer Learning: A Case Study of Wuhan, China",2025.0,Sustainability,Yanhua Chen; Zhi-Ri Tang,"Human perception of urban streetscapes plays a crucial role in shaping human-centered urban planning and policymaking. Traditional studies on safety perception often rely on labor-intensive field surveys with limited spatial coverage, hindering large-scale assessments. To address this gap, this study constructs a street safety perception dataset for Wuhan, classifying street scenes into three perception levels. A convolutional neural network model based on transfer learning is developed, achieving a classification accuracy of 78.3%. By integrating image-based prediction with spatial clustering and correlation analysis, this study demonstrates that safety perception displays a distinctly clustered and uneven spatial distribution, primarily concentrated along major arterial roads and rail transit corridors by high safety levels. Correlation analysis indicates that higher safety perception is moderately associated with greater road grade, increased road width, and lower functional level while showing a weak negative correlation with housing prices. By presenting a framework that integrates transfer learning and geospatial analysis to connect urban street imagery with human perception,",1.0,https://doi.org/10.3390/su17177641
ea2a4ef5a345eaf502a345d1ab7a207a1f8fb460,10.54097/hbem.v19i.11880,Analysis of Big Data Application in Real Estate Investment,2023.0,"Highlights in Business, Economics and Management",Yi-Fan Guo,"As an important project of national economic growth, real estate plays an important role in education, scientific research and production industries. Despite the slower pace of urbanization, However, the development of real estate has started to slow down currently, and there have been problems such as market differentiation and changes in consumer demand. At the same time, many investors' investments are blindly aimed at adapting to the market, which can easily lead to losses. With the development of the information age, big data can process massive amounts of information and provide investors with effective reference when investing in real estate, thereby improving the efficiency and gold content of real estate investment. This study will describe the application of big data in real estate investment from four aspects: information mining system, housing price evaluation, risk profile and marketing. The information mining system is mainly used in the initial stage of development to provide decision-makers with the preferences of the target customer group. House price prediction and risk prediction are mainly based on BP neural networks. Marketing mainly describes the application o",1.0,https://drpress.org/ojs/index.php/HBEM/article/download/11880/11564
6d83eb8f86dfbea58c35e5fd3c10213103c156cb,10.1016/j.chiabu.2025.107649,Geospatial variation of CPS reports involving school-aged children by reporter type.,2025.0,International Journal of Child Abuse & Neglect,Rebecca Rebbe; A. Eastman; John J. Prindle; David Ansong,"BACKGROUND
Child protective services (CPS) rely on referrals of child maltreatment to address abuse and neglect. CPS referral rates vary geographically in the type of maltreatment reported and community characteristics.


OBJECTIVE
This study examined the relationship between CPS referral rates and community characteristics, based on reporter type and geography. We investigated (a) the relationship between CPS referral rates and community-level characteristics, including how these relationships vary by reporter type; and (b) the relationships between community-level characteristics and CPS referral rates across geographic areas.


PARTICIPANTS AND SETTING
The study used statewide data from California on CPS referrals for children aged 5 years or older from 2018 to 2022.


METHODS
Referrals were geocoded to census tracts based on the family's address. Regression models were run for each reporter type, using referral rate as the outcome and community-level social determinants of health indicators from the Healthy Places Index (economic, education, health insurance, clean environment, housing, neighborhood conditions, social, transportation, racial composition, and urbanicity). Geogra",1.0,
9ffc586f651b3035ba2f9b5866eb0451f65ecbd1,10.38100/jhuf.2024.9.1.31,"Analysis of the impact of urban planning facility locations on
 multifamily attached housing prices*",2024.0,Journal of Housing and Urban Finance,Yujeong Hwang; Y. Choi,"This study examines the correlation between multifamily attached housing prices and the significance of urban planning facilities. Using data on 12,228 actual transaction prices in Busan for 2022, provided by the Ministry of Land, Infrastructure, and Transport, we conducted an empirical analysis to identify factors affecting multifamily attached housing prices. To precisely analyze the regional characteristics at higher levels, we employed a Multilevel Model, which allows for hierarchical structuring and analysis. The level-2 regional factors in the model include area ratios of public, cultural, sports, spatial, transportation, distribution, supply, disaster prevention, health and hygiene, and environmental facilities. At the level-1 individual factors, all variables except for the floor area ratio significantly influenced multifamily attached housing prices. Positive effects were observed for all variables except for area and building age. At level-2, which considered the group characteristics of the lower-level data, spatial and transportation facilities were significant. Spatial facilities had a positive (+) effect, while transportation facilities unexpectedly showed a negative ",1.0,http://www.ejhuf.org/download/download_pdf?pid=jhuf-9-1-31
d72eccd75ed1a2435c718310120633a3a6e23965,10.48175/ijarsct-25857,Evaluating Machine Learning and Deep Learning Models for Housing Price Prediction: A Review,2025.0,"International Journal of Advanced Research in Science, Communication and Technology",S. Wawge,"In the world of the housing market, a dynamic and sensitive market, high dependence on machine/Deep learning (ML DL) for price prediction has been observed. This research indicates that certain machine learning and deep learning algorithms, including support vector machines, LSTM, RNN, decision trees, random forests, and linear regression, may detect both linear and nonlinear correlations in housing data. In this way, these models have become helpful to stakeholders like homeowners, investors, and urban planners for predicting the prices accurately and understanding the market trends. Model overfitting is still a challenge; feature selection and accuracy of the data still persist. In the future, research could be conducted on how to integrate many data, from real market trends to multimodal data such as property images in order to improve prediction accuracy. Further, hybrid models that combine the best of all in algorithms, as well as reinforcement learning and sophisticated optimization strategies, could also increase the ability and performance. Nonetheless, fairness and bias in automated systems have to be another part to take into consideration to assure an equitable result fo",1.0,https://doi.org/10.48175/ijarsct-25857
28818d27f380d2060451ca0c102eca6b4a020897,10.7251/joce2408033o,Impact of Macroeconomic and Environmental Factors on Household Debts in G7 Countries,2024.0,JOURNAL OF CONTEMPORARY ECONOMICS,Nasiha Osmanović; S. Alvi,"This article aims to study the influence of macroeconomic and ecological factors on household debts. The quantitative method of OLS multiple Regression is applied to the data used for this research from the period 2003 to 2021. To establish the macroeconomic factor, the metrics are the real house price index with the base year 2015, unemployment as % of the labour force, and short-term interest rate per annum. Research shows that house price and unemployment have a noteworthy relationship with the dependent variable, while trade in goods significantly adversely affects household debt. In respect to the climate change factors, results reveal that annual surface temperature and annual emissions of CO2 have a favourable and notable impact on household debt. The only insignificant effect on household debts had been shown by the short interest rate. The beneficiaries of this research can include policymakers, economists, and financial institutions who are interested in understanding the factors that affect household debts. The significant relationship between household debt and factors such as house prices, unemployment, export trade, climate change factors, etc. reflect underlying econ",1.0,https://www.swotjournal.com/index.php/casopis/article/download/11/9
bf5e071cf2e57282a9243345a3da467edf1fd50a,10.1108/ijhma-02-2022-0022,Housing market price movements under tech industry expansion during COVID-19,2022.0,International Journal of Housing Markets and Analysis,J. Jiao; Mira R. Bhat; A. Azimian; Akhil Mandalapu; A. Farahi,"
Purpose
This study aims to analyze the impact of technology-based corporation relocation on housing price indices during COVID-19 within the metropolitan areas of Austin, Texas and Seattle/Bellevue, Washington.The corporations under observation were Tesla and Amazon, respectively. The analysis intends to understand economic drivers behind the housing market and the radius of its effect while including fixed and random effects.


Design/methodology/approach
This study used a difference-in-difference (DID) method to evaluate changes in housing price index near and further away from Tesla’s and Amazon’s new corporate locations. The DID method allows for the capture of unique regional characteristics, as it requires a treatment and control group: housing price index and 5-mile and 10-mile search radii centered from the new corporate location.


Findings
The results indicated that corporate relocation announcements had a positive effect on housing price index post-pandemic. Specifically, the effect of Tesla’s relocation in Austin on the housing price index was not concentrated near the relocation site, but beyond the 5- and 10-mile radii. For Seattle/Bellevue, the effect of Amazon’s re",1.0,
104bd4a8f4a36a1959c10f8c9721691d25a69344,10.29207/resti.v9i4.6619,Comparing Optimization Algorithms in ANN Models for House Price Prediction in Pekanbaru,2025.0,Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi),Doni Winarso; Edo Arribe; Syahril; Aryanto; Muhardi; S. Musa,"This study evaluates the performance of five optimization algorithms in Artificial Neural Network (ANN) models for predicting house prices in Pekanbaru. The optimizers tested include Adam, AdaDelta, Stochastic Gradient Descent (SGD), Nadam, and Adaptive Sharpness-Aware Minimization (ASAM). A total of 3,149 house sales records were collected from rumah123.com between January and December 2024. After cleaning 148 incomplete entries, 3,001 valid records remained. The dataset included seven features: price, location, number of bedrooms, number of bathrooms, land area, building area, and garage capacity, with the location encoded using one-hot encoding. The research involved a literature review, problem formulation, data acquisition, preprocessing, model development, and evaluation. Model performance was assessed using the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). The results show that SGD consistently achieved the best performance, particularly at a 90:10 train-test split, with the lowest MAPE (1.74%) and MSE (0.3279). Adam and Nadam also performed well, while ASAM had the highest error (MAPE 6.14%). These findings indicate tha",1.0,https://jurnal.iaii.or.id/index.php/RESTI/article/download/6619/1109
c181e7511d727b79f219d28a38a187c48c581102,10.1080/01603477.2025.2588748,Determinants of the Portuguese external imbalances: the lens of post-Keynesian economics,2025.0,Journal of post Keynesian economics,Ricardo Barradas; João Alcobia,"Abstract Portugal has historically displayed strong external imbalances, which have already resulted in three requests for international financial assistance from the International Monetary Fund, the European Commission and/or the European Central Bank. Stemming from the framework of mainstream economics literature, these institutions imposed demanding austerity measures based on internal devaluation, huge wage restraint policies and strict control on government balance in order to contain the Portuguese domestic demand and to promote Portuguese external competitiveness. Accordingly, Portuguese labor costs have registered a general decreasing trend, and government balance has substantially improved in the last four decades, but the Portuguese external imbalances remain unsolved, which suggests that we need to go beyond the conventional economic literature in order to better ascertain the right determinants of external imbalances. This paper aims to contribute to the current theoretical debate about the determinants of the external imbalances by relying on the post-Keynesian economics literature and performing a time series econometric analysis for Portugal from 1988 to 2023. Result",1.0,http://hdl.handle.net/10400.5/31344
83aa251387b01c894d0241279ae0ee1e2da23b53,10.51865/eitc.2024.04.06,Analysis of Housing Prices in BRICS Countries: COVID-19 Pandemic Shock,2024.0,Economic Insights: Trends and Challenges,X. Shodrokova; I. Asngari,"The study analyzed the impact of the COVID-19 pandemic, inflation, and interest rates on the Home Price Index (HPI) in BRICS member countries (Brazil, Russia, India, China, and South Africa), using data from Q1 2016 to Q2 2023. Secondary data used in this study were obtained from official sources, including the International Monetary Fund (IMF), the Central Bureau of Statistics (BPS), and the Central Banks of BRICS countries. The analysis technique in this study uses panel data regression, namely the Fixed Effect Model (FEM). The results of the analysis of the data panel show that COVID-19 has had a significant positive impact on the HPI, indicating that the pandemic has affected the housing market in the BRICS countries. Interest rates have a negative and significant impact on the HPI, suggesting that tighter monetary policy is likely to suppress rising house prices. Finally, inflation has a positive and significant impact on the HPI, illustrating a positive relationship between inflation rates and house prices in BRICS countries. These findings provide insights for policymakers and stakeholders in BRICS countries in managing housing markets and addressing challenges posed by unfo",1.0,https://doi.org/10.51865/eitc.2024.04.06
4b59e1e5c66091609bd65d9f3681ccfd5fbc7390,10.54254/2754-1169/2024.ld19612,The Research on Influencing Factors of Housing Price,2025.0,"Advances in Economics, Management and Political Sciences",Kunlong Du; Yanyi Li; Ruike Liu,"This paper analyzed the various factors affecting housing prices in the Boston area. Given that housing prices are one of the most highly influential elements in the social economy and individual decisions, this study aimed to trace the main key determinants driving fluctuations in this market. The results from an MLR model applied on 509 observations of characteristics describing housing in the Boston area, using a dataset from Kaggle against housing prices, have been reported. The empirical results of the study reveal that crime rate, number of rooms, air quality (nitrogen oxide concentration), pupil-teacher ratio, property tax rate, distance to employment centers, and highway accessibility affect housing prices. A higher crime rate and nitrogen oxide concentration lead to a decrease in housing prices, whereas an increase in the number of rooms and better highway accessibility leads to an increase in housing prices. Other variables that the study identified as likely to have multicollinearity problems are property tax and highway accessibility. Such conclusions assist in gaining a better understanding of the dynamics associated with determinants in housing price and offer invalua",1.0,https://www.ewadirect.com/proceedings/aemps/article/view/19612/pdf
7c96947cd1757b80a22af52aa821463e6a63d68b,10.1515/remav-2016-0010,The Impact of Wind Farms on the Prices of Nearby Houses in Poland: A Review and Synthesis,2016.0,,Marcin Torzewski,"Abstract Since 2009/10 Poland has experienced a dynamic growth of wind energy production. Currently, wind energy is the most popular resource of renewable energy in Poland. Despite the importance of wind energy for the Polish economy, there is no reliable and comprehensive research on the causal effects of wind farm facilities on property prices. The aim of this article is to critically review existing research related to the impact of wind farms on prices of nearby residential properties. The article concentrates on Polish as well as international scientific literature. Besides presenting the main findings obtained from the review of literature, the article clusters these findings based on information that is crucial to the analysis: methodology, data, and subject of study. The literature review is followed by a description of the characteristics of wind energy in Poland, which includes differences in the location of wind farms between Poland and other countries. The article concludes that despite the fact that some international research points to a significant causal effect of wind farms on property prices, these results cannot be easily transposed to Polish circumstances. Based",1.0,https://content.sciendo.com/downloadpdf/journals/remav/24/2/article-p13.pdf
edd222f8c89bdf34b560a44b9091d2c544558107,10.5220/0012999000004601,Predicting New York Housing Prices: A Comparative Analysis of Machine Learning Models,2024.0,"Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy",Jie Yu,": Accurate prediction of housing prices in New York City is crucial for investors, policymakers, and consumers navigating one of the most volatile housing markets. This study explores various machine learning methods to forecast housing prices in New York City. The predictive power of Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), and XGBoost (XGB) was examined using a comprehensive dataset with diverse housing attributes. Our results show that LR and SVR provided less accurate predictions, with LR achieving an RMSE of 4,091,594, a MAPE of 1.2991, and an adjusted R-squared of 0.2642, while SVR had an RMSE of 4,967,168, a MAPE of 0.7753, and an adjusted R-squared of -0.0844. In contrast, ensemble methods, namely RF and XGB, demonstrated superior performance on all accounts. RF achieved an RMSE of 2,145,123, a MAPE of 0.3086, and an adjusted R-squared of 0.7978, while XGB had an RMSE of 2,483,884, a MAPE of 0.4163, and an adjusted R-squared of 0.7288. These results conclude that ensemble methods, which can handle complex datasets with higher dimensionality and noise, are more adept at predicting housing prices in varied markets such as New York City. The",1.0,http://dx.doi.org/10.5220/0012999000004601
dd66ababd8c0664a0448b289b5e02a3f5a89778c,10.54254/2754-1169/66/20241217,Evaluating the Impact of Green Gentrification -A Case Study of London,2024.0,"Advances in Economics, Management and Political Sciences",Xi-Yuan Wang; Dong-Zhen Yuan,"In this research, we utilize the Ordinary Least Squares (OLS) regression method to explore the relationship between green gentrification and key socioeconomic factors within London's urban landscape. Our aim is to delve into the impact of introducing or expanding green spaces on residents' lives, particularly in relation to income, life expectancy, well-being, housing costs, and unemployment rates. Through the use of OLS regression and an extensive dataset sourced from empirical evidence, we quantitatively analyze the connections between green gentrification and variations in these socioeconomic variables across different neighborhoods. Our findings reveal noteworthy associations, highlighting positive correlations between green spaces and well-being, life expectancy, and house prices, while also noting a negative correlation with the unemployment rate. These insights contribute to a comprehensive understanding of the complex dynamics between urban development, environmental enhancements, and societal well-being, emphasizing the potential advantages of green gentrification and endorsing inclusive policies. Grounded in the OLS method, our research strengthens the empirical foundatio",1.0,https://www.ewadirect.com/proceedings/aemps/article/view/9351/pdf
538168405467d5179fb363d0e5fe8e7cf715b1b8,10.59720/22-062,The effect of COVID-19 on the USA house market,2022.0,Journal of emerging investigators,W. Xiao; Linying Gong,"The purpose of this study was to understand the varying effects of COVID-19 on the median house price in metropolitan areas in the USA. Recently, the demand for houses has increased due to the COVID-19 pandemic. The pandemic increased the desire to stay indoors and introduced working from home. Four different factors were tested for their impact on the housing market: number of COVID-19 deaths, number of available houses for sale, unemployment rate, and mortgage interest rates. The control in this study was 2018-2019 house price data. We hypothesized that the house supply would cause the greatest change in house prices. Ckmean clustering method was applied to separate 93 metropolitan areas of the USA into three groups with different median house price levels. We then conducted linear regression modeling for each group to determine the varying level of effect each of the variables had. The results suggested that unemployment rate had the largest correlation with house prices, especially for metropolitan areas with extremely high house prices. This result did not support the research hypothesis. Further research could clarify the findings of our study by using fine-turned modeling te",1.0,https://emerginginvestigators.org/articles/22-062/pdf
7aa0fe95a2fc31b97bdda4535535c59d199fc10c,10.3390/su152316495,Do Public-Led Housing Site Development Projects Affect Local Housing Prices: A Proposal for a Comprehensive Policy Evaluation Methodology,2023.0,Sustainability,Jina E. Kim; S. Jee; Seunghan Ro,"As the population in urban areas increases, the importance of adequate public-led development policies for sustainable cities with affordable housing is becoming more highlighted. In this regard, this study aims to determine the effectiveness of public-led urban development policies for sustainable growth in urban areas, specifically measuring the effect of housing site development projects on housing prices. The geographical scope of the study is the project sites and their surrounding areas in South Korea, and the temporal background is from 2006 to 2023. The project sites were subdivided into four groups by using the Self-Organization Map (SOM), a machine-learning-based clustering analysis, to collect characteristics of each region. Then, the impact of the policy and the prediction of the real estate market of each cluster were analyzed by applying the DID and LSTM models, which have recently been proven to show a high validity. The results show that each cluster had different characteristics and effects from the development projects, depending not simply on the location, but on several characteristics, including the level of size, infrastructure installation, input cost, etc. F",1.0,https://www.mdpi.com/2071-1050/15/23/16495/pdf?version=1701441118
dc35a0cc02a20045ba88b28fb9c28cd847fa4673,10.1109/ubmk55850.2022.9919540,Reviewing the Effects of Spatial Features on Price Prediction for Real Estate Market: Istanbul Case,2022.0,2022 7th International Conference on Computer Science and Engineering (UBMK),M. İ. Ecevit; Z. Erdem; H. Dağ,"In the real estate market, spatial features play a crucial role in determining property appraisals and prices. When spatial features are considered, classification techniques have been rarely studied compared to regression, which is commonly used for price prediction. This study reviews spatial features' effects on predicting the house price ranges for real estate in Istanbul, Turkey, in the classification context. Spatial features are generated and extracted by geocoding the address information from the original data set. This geocoding and feature extraction is another challenge in this research. The experiments compare the performance of Decision Trees (DT), Random Forests (RF), and Logistic Regression (LR) classifier models on the data set with and without spatial features. The prediction models are evaluated based on classification metrics such as accuracy, precision, recall, and F1-Score. We additionally examine the ROC curve of each classifier. The test results show that the RF model outperforms the DT and LR models. It is observed that spatial features, when incorporated with non-spatial features, significantly improve the prediction performance of the models for the house ",1.0,https://hdl.handle.net/11729/5362
5555039dff073c5150fe86e0865ebf54c43f247c,10.3390/e27070715,Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach,2025.0,Entropy,Rong-Shang Chen; Zhiyong Chen,"Spatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions. We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model for spatial data to improve the prediction of performance. It can be used to capture the response of covariates to linear and nonlinear effects at different quantile points. Through an approximation of the nonparametric functions with free-knot splines, we develop a Bayesian sampling approach that can be applied by the Markov chain Monte Carlo (MCMC) approach and design an efficient Metropolis–Hastings within the Gibbs sampling algorithm to explore the joint posterior distributions. Computational efficiency is achieved through a modified reversible-jump MCMC algorithm incorporating adaptive movement steps to accelerate chain convergence. The simulation results demonstrate that our estimator exhibits robustness to alternative spatial weight matrices and outperforms both quantile regression (QR) and instrumental variable quantile regression (IVQR) in a finite sample at different quantile",1.0,https://www.mdpi.com/1099-4300/27/7/715/pdf?version=1751376047
4262498469e92b69370c27821a2fd7bec69f36d5,10.3389/frevc.2025.1658608,Beyond average preparedness: a quantile analysis of earthquake mitigation behaviors among homeowners,2025.0,Frontiers in Environmental Economics,Florent Nkouaga,"
 
 This study investigates earthquake preparedness among homeowners in eight states affected by the New Madrid Seismic Zone, using the 2024 NAIC earthquake survey data.
 
 
 
 Applying weighted Ordinary Least Squares (OLS) and quantile regression analyses, the research explores how risk perception, community engagement, and earthquake awareness influence preparedness across varying readiness levels.
 
 
 
 Results highlight community engagement as the strongest predictor across all preparedness quantiles, emphasizing its role particularly at higher preparedness levels. Significant interaction effects were observed between earthquake awareness and home value, with heightened preparedness evident among informed, higher-value property owners. Conversely, the interaction between income and tenure indicates diminishing preparedness among higher-income, long-term residents, suggesting complacency effects.
 
 
 
 These findings underscore the need for differentiated preparedness strategies that accommodate varying socioeconomic conditions and preparedness levels. This nuanced approach offers policymakers critical insights for enhancing resilience by tailoring interventions to specific ho",1.0,https://public-pages-files-2025.frontiersin.org/journals/environmental-economics/articles/10.3389/frevc.2025.1658608/pdf
7d1cab9cb4a23353a91e331ad601c8e52aeb0810,10.4108/eai.18-11-2022.2327165,Research on Housing Price Forecasting Model Based on Multiple Linear Regression Model and Neural Network Model,2023.0,"Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022, Nanjing, China",Rui-Hong Xu,"——There is a huge demand for the housing market in China. Effective forecasting models can provide reference for potential house buyers, thus avoiding blind purchase. In addition, policymakers can adjust real estate policies based on model predictions to reduce the stagnancy of the real estate markets, prevent the occurrence of economic crisis and maximize the benefits1.[1] Based on the second-hand housing data of Bao 'an District, Shenzhen city, this paper establishes multiple linear regression model and neural network model respectively, and tests the model with test set data, and obtains the conclusion that the neural network model is better in the accuracy of describing experimental data and fitting effect. Besides, this paper studies the role of multiple linear regression models in helping consumers to buy houses on demand and in helping real estate practitioners to set house prices according to influencing factors. The multiple linear regression model can help consumers and real estate practitioners to intuitively understand the factors that significantly affect housing prices. Furthermore, consumers can combine their own needs based on these factors, select the houses with c",1.0,http://eudl.eu/pdf/10.4108/eai.18-11-2022.2327165
701f7563ea70de153f3bb30c96bc37a70ccaaec5,10.54254/2754-1169/2024.18705,Comparative Analysis of the Effectiveness of Different Algorithms for House Price Prediction in Machine Learning,2024.0,"Advances in Economics, Management and Political Sciences",Zhicong Deng,"In today's economic climate, where housing is a major focus, predicting house prices has become essential for both buyers and investors. Accurate price prediction allows for more informed decision-making, helping to minimize losses and maximize potential profits. The paper explores the different machine learning algorithms to predict house prices, utilizing the classic Boston Housing dataset from Kaggle. The study compares the performance of four algorithms: Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), and Extreme GB (XGBoost). Experimental results demonstrate that GB and XGBoost outperform the other models, delivering the highest prediction accuracy. These algorithms excel in capturing complex, nonlinear relationships in the data, making them particularly effective for house price prediction. The findings suggest the models like GB and XGBoost can enhance the accuracy of prediction, offering valuable insights into the real estate market. The study also highlights the importance of continuously refining machine learning models to adapt to changing market conditions. By improving predictive models, the study contributes to an understanding of home price dynamics, ",1.0,https://www.ewadirect.com/proceedings/aemps/article/view/18705/pdf
b7797999153edec8212df5ef5955cc5c1ee405b3,10.64764/ijietas.v2.i1.05,House Price Prediction Using Machine Learning Techniques,2026.0,International Journal of Innovations in Engineering Technology and Applied Sciences,M. Roy; Taufeeq Ahmad,"Prediction of housing market prices is critically important because many sectors of the housing market rely on accurate house price forecasts to make decisions about investments and financing. Therefore, this study presents an extensive analysis of the prediction of residential property prices in Delhi through using a machine-learning approach to connect multiple sources of information from the housing sector, including a curated database of housing sales in Delhi, geographical locations such as locations to metro stations, schools, hospitals, etc., and local environmental quality (i.e., air quality index). Through feature engineering and through the use of a Random Forest regressor trained with 7738 records of housing sales... The Random Forest regressor trained on 7738 records of housing sales has provided good performance with an R² of approximately 0.92 when tested against the test dataset when forecasting sale price for individual residential properties. Res1lts show that including data about location-related amenities (such as subway stations) and air quality significantly increases predictive accuracy of the sale prices of the houses in Delhi. An description of the models' o",1.0,https://ijietas.com/index.php/files/article/download/39/17
96a3b0599430241ba644e3cad294e999b0c5562b,10.54097/q6kz2d72,Paris House Rental Price Index Prediction-A Classical Statistical Model Approach,2024.0,Highlights in Science Engineering and Technology,Xiaopeng Zhang,"The study focuses on predicting rental prices in Paris and aims to contribute to urban economics and data analytics. It analyzes a wide range of data sources, including historical rental prices, economic indicators, demographics, and regulations. The goal is to compare classical statistical models' prediction accuracy of these three models: ARIMA, dynamic regression, and random forest. The results reveal that the ARIMA model performs best, offering more accurate predictions. ARIMA relies on time series analysis, capturing complex patterns in rental prices, making it well-suited for dynamic real estate markets. The study also examines the impact of external factors like rent reference indices, house price indices, and unemployment rates on prediction accuracy. While these factors seem promising, the further analysis suggests they can introduce noise into predictions if not chosen carefully. In conclusion, this research contributes valuable insights for rental market stakeholders. The ARIMA model proves effective for rental price forecasting in Paris, emphasizing the importance of understanding intrinsic time series patterns. This study can guide decision-making for renters, landlord",1.0,http://dx.doi.org/10.54097/q6kz2d72
c970dcc688eb2f5bd1529a98ed4de09ee1882ae8,10.61506/01.00513,The Role of Various Factors in Accumulation of Household Debt on OECD Countries,2024.0,Bulletin of business and economics,Abdul Aziz; Shumaila Israr; Sadiya Amin; Bisma Ashraf; H. Tahir,"The rapid increase in household debt since the 1980s has significantly transformed economic environments and raised substantial concerns regarding financial stability, particularly in the aftermath of the 2008 global financial crisis. This research seeks to explore the underlying factors contributing to household debt accumulation across 30 OECD nations, examining the impacts of both demand and supply-side elements. The analysis utilizes a baseline regression model to assess the determinants of household debt, employing fixed effect (FE), random effect (RE), and panel corrected standard error (PCSE) estimators to ensure robustness over the period from 1995 to 2022. The findings of this study provide novel insights into the drivers of household debt. Additionally, the results indicate that income, interest rates, financial development, unemployment, housing prices, welfare expenditures, and the working population all exert positive and significant influences on household debt, while inflation is found to have a negative impact. Consequently, the study advises policymakers to exercise caution and take proactive measures to prevent household indebtedness from escalating to unsustainab",1.0,https://bbejournal.com/index.php/BBE/article/download/1023/1136
69288e963b8055b19e0fd077000b14eb34270ae1,10.3846/ijspm.2024.20899,THE EFFECTS OF TRANSACTION HOTSPOTS AND FLIPPING HOTSPOTS ON HOUSING PRICES,2024.0,International Journal of Strategic Property Management,Bor-Ming Hsieh; Chih-Yuan Yang,"In contrast to much of the literature that focuses on the issue of spatial dependence in housing price research, this study addresses the spatial aggregation of housing transactions and analyzes the effects of transaction hotspots and short-term flipping hotspots on housing prices by using real housing transaction data in Taipei City, Taiwan. The empirical results show that after controlling for the effects of spatial dependence and individual housing attributes, the impact of transaction hotspot areas on housing prices is significantly negative, while the impact of flipping hotspot areas on housing prices is significantly positive. The results verify that the key to driving up housing prices lies in flipping activities. Furthermore, the results of the spatial quantile regression model show that low-priced residential properties are more sensitive to the spatial concentration of housing transactions and flipping transactions in the housing market. Our results have implications for the government’s policy intending to control hot trade volumes to cool skyrocketing housing prices in a booming housing market. It is suggested that the government should pay attention to restraining shor",1.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/20899/12187
b99a736d75227e6fa23783d72f55fa3764a3aeb8,10.2991/ifmeita-16.2016.25,Residential Choice Based on Traffic and Location Fuzzy Attributes,2016.0,,Lin Zhang; Dina Xia; Na-N. Lv,"In order to explore the influence factors of residential choice, we established the housing valuation model by discussing the four factors of the completeness of facilities, quality of public services, location environment, accessibility evaluation index of each district. By the contract mode, we could obtain the utility of housing price of each family. Through impedance of different traffic modes, we could obtain the generalized transportation cost of family. According to the family influenced by housing price, influence factors of the district and traffic, the nonlinear programming model was established which was to find the best district for family. Examples show that: higher-income family more willing to choose the residence base on the good completeness, high quality of public services and good location environment, such as district 1, 2, 4, 6, 14. Low-income family is more inclined to choose the higher utility of housing and accessibility, such as the district 7, 9, 16, 21. This shows that the important of residential choice by the various district factors, the housing prices and the traffic. The combination of fuzzy and optimization to consider the choice of residence, not o",1.0,https://download.atlantis-press.com/article/25850331.pdf
a8dcb4a47847d7ad3630981d5ab5b4f61e00cddc,10.3390/urbansci8030137,"The Relationship between Urban Functional Structure and Insomnia: An Exploratory Analysis in Beijing, China",2024.0,Urban Science,Si-Rui Chen; Li-Jun Xing; Yu Liu; Ji-Wei Xu,"Insomnia is a prevalent sleep disorder that causes serious harm to individuals and society. There is growing evidence that environmental factors may be associated with sleep disorders, but few studies have explored the relationship between insomnia and urban functional structure from a spatial perspective. This study collected multi-source big data (e.g., insomnia posts on Weibo, locations of urban facilities on Baidu) and explored the effects of different urban spatial element configurations on residents’ insomnia. The ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to quantify the global and local effects of urban functional categories on residents’ insomnia. The results showed that the quantity of catering service facilities accounted for the largest proportion, and the consumer function was the most consistent with the distribution of insomnia. There is a domain relationship between the incidence of insomnia and urban functional zones. It has the strongest correlation with employment mixed functional zones and the weakest with residential mixed functional zones. These findings could serve as references for the functional structure and ",1.0,https://doi.org/10.3390/urbansci8030137
3b46300724c1e5ee1c33d1686e3898c7227985d2,10.1353/sgo.2022.0028,"Does Municipal Incorporation Influence Levels of Black-White Segregation? Evidence from Counties in the Southern United States, 1990–2010",2022.0,Southeastern Geographer,Russell M. Smith; Zachary D. Blizard,"<p>highlights:</p><list list-type=""bullet""><list-item><label>•</label><p>Municipal incorporation is positively related to the change in Black-White segregation across southern counties.</p></list-item><list-item><label>•</label><p>Holding all else constant, new city formation increases Black-White county-level segregation by 2 percent.</p></list-item><list-item><label>•</label><p>Possible effects on a proposed municipality’s neighbors may be a key issue to consider during the approval process.</p></list-item></list><p>abstract:</p><p>Over 400 municipalities have been created since 1990, the majority of which are in the American South. Though municipal incorporation can bring positive changes to a locality, such as higher home values and economic growth, the creation of new cities leads to more fragmented political geographies resulting in negative outcomes like inefficient resource and land use. Recent studies of newly incorporated municipalities (NIMs) find that these newly created cities tend to be either majority White or majority non-White or BIPOC. This type of Tieboutian residential sorting has been linked to increased levels of racial segregation in some areas of the US with",1.0,
89320da66f3ce980a47b29c40bdd0d9763125dd3,10.54097/cvn9mr84,Integrating Machine Learning Techniques for Real Estate Analysis,2024.0,Highlights in Science Engineering and Technology,Ze-Kun Chen,"Machine learning can be applied to prediction models. The study delved into house price prediction, an area of significance to both individual participants and policymakers within and beyond the real estate market. The advanced predictive model is a tool that informs quality decision-making. This study synthesizes and builds on existing research to implement and enhance house price prediction methods, assessing the performance of Convolutional Neural Networks (CNNs), Decision Trees, and K-Nearest Neighbors (KNN) which is of focus on Most Correlated Features (KNN-MCF). Although these techniques have been developed and refined over the years, they still face challenges such as overfitting, noises existing in the dataset, and the complexity of modeling both linear and non-linear relationships. By integrating the resources, the study aims to provide objective guides for addressing the common challenges and predicting house prices, illustrating the appropriate analytical methods to apply based on characteristics of a database, extra space for improvements, and critical concerns of their practical applications. Ultimately, the research strives to bridge the gap between theoretical models",1.0,http://dx.doi.org/10.54097/cvn9mr84
be7ea8aa07f1367587ad13335eec8d39dd87890c,10.1055/a-2516-1692,Hospital Health Information Exchange Network Density and Predictors Across U.S. Hospital Referral Regions,2024.0,Applied Clinical Informatics,Sunny C. Lin; K. E. Maddox; Daphne Lew; J. Adler-Milstein; T. Kannampallil,"Abstract Objectives  This study aimed to develop a measure of health information exchange (HIE) for characterizing the density of inter-hospital HIE connections and identify regional characteristics associated with HIE network density. Methods  HIE network density was measured as the proportion of hospital pairs within a region that are connected through HIE. The 2022 American Hospital Association's Information Technology Supplement survey was used to calculate HIE network density for US hospital referral regions (HRRs). Bivariate tests and multivariable regression were used to characterize hospital, electronic health record (EHR) vendor, and resident characteristics associated with HIE network density. Results  Data on 2,509 hospitals across 274 HRRs were included in the study, with 92% of hospitals participating in at least one HIE. On average, hospitals participated in two HIEs and there were seven HIEs present in each region. HIE network density ranged from 0.0 to 1.0, with a median of 0.78 and an interquartile range (IQR) of 0.51–1.00. Hospital and vendor characteristics associated with greater HIE network density include: more HIEs per hospital, a higher proportion of non-pro",1.0,
e8684a8e2253ff3510422571ee7417560dabc237,10.21834/aje-bs.v3i9.298,Influencing Characteristics for Housing Product in Jakarta Metropolitan Area,2018.0,Asian Journal of Environment-Behaviour Studies,R. A. Rahadi; S. K. Wiryono; Deddy P. Koesrindartoto; I. B. Syamwil,"This research paper will try to analyze the relationship between influencing characteristics for housing product price in Jakarta Metropolitan Region. The aforementioned characteristics are viewed from the property practitioner perception. To find the answer for this research paper questions, seven property practitioners were interviewed to test the initial hypotheses. The findings suggest that brands, facilities, developer reputations, pricing policies, reinvestment values, and speculative behavior play significant parts as internal and external attributes for housing price in Jakarta Metropolitan Region. 
Keywords: Real estate; property; consumer preferences; property practitioner perception. 
eISSN 2514-751X © 2018. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Univers",1.0,https://aje-bs.e-iph.co.uk/index.php/ajE-Bs/article/download/298/284
d681bec1fde1191e7859067c4344bf6668a677c6,10.1101/2023.12.20.23300287,Understanding the Easterlin Paradox in China: Investigating the Psychological Stress and its Relation to Nonincome Factors,2023.0,medRxiv,Zhao-Yi Zeng; You-Qing Chen; Wen-Liang Su,"Although China's economy and living conditions have improved significantly in the past few decades, previous studies have found that residents' happiness has not kept pace with them accordingly. Such an Esterlin paradox may partly be due to the increase in psychological stress in the progress of China's urbanization. To verify this point, we constructed an index of psychological stress (IPS) through daily internet search queries of four psychological stress-related keywords obtained from the Baidu Index from 2011 to 2021. The IPS for each province, municipality, and main city during 2018-2019 was also calculated. Meanwhile, the city level's economic factors (GDP per capita, income of employees), living costs (housing prices, commuting time), and living environment quality (air quality, environmental noise) were employed to predict the IPS. The results showed that the IPS in China showed an upward trend from 2011 to 2021, and it was higher in the eastern coastal regions than in other regions. Housing prices and commuting time were the only significant predictors of IPS in the hierarchical regression. Our results suggested that the Easterlin paradox in China may be explained by the r",1.0,https://www.medrxiv.org/content/medrxiv/early/2023/12/21/2023.12.20.23300287.full.pdf
b82770dd55b61c748bb1256b94e14cbfab24fbf4,10.21834/e-bpj.v2i5.703,Exploring the Effect of Green Element on Condominium Price in Penang,2017.0,,Lizawati Abdullah; T. Mohd; A. Nawawi; N. Ismail,"The emphasis of this research is exploring the green element effect on the price of residential properties. This research identified the prices difference between GBI and non-GBI residential, further demonstrates the relationship between price and green by utilizing Multiple Regression Analysis. The samples are 471 condominiums in Penang derived from the database of NAPIC. The empirical result of the research shows that a residential building having GBI certification increased the property price by RM1009.079. Findings of this research contribute to the significant relationship between green element and condominium price, as well as indicate the impacts of GBI on the price of properties in Malaysia. Keywords: green, GBI, condominium, regression ISSN: 2398-4287 © 2017. The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of ",1.0,https://doi.org/10.21834/e-bpj.v2i5.703
c7c9f06acf19f85e5a725174eed9e05f5e25899a,10.21776/csefb.2022.01.3.09,TINGKAT KEPEMILIKAN RUMAH DI INDONESIA,2022.0,"Contemporary Studies in Economic, Finance and Banking",Kintan Terate Sari; Atu Bagus Wiguna,"Government policies related to public housing have not been able to overcome the high backlog of home ownership in Indonesia. This is constrained by various problems such as housing affordability and differences in access to housing due to gaps in infrastructure development between regions in Indonesia. This study aims to analyse the effect of credit and residential property prices, income and unemployment on the home ownership rate in Indonesia. Using a descriptive quantitative approach with 3 models estimated by the multiple regression method of panel data.The secondary data used is aggregate data of provinces in Indonesia. The home ownership rate is higher in Eastern Indonesia compared to Western Indonesia, this is due to the unemployment rate in Western Indonesia which is higher than Eastern Indonesia. Although assisted by a higher average residential property credit than the average residential property credit in Eastern Indonesia does not make the average home ownership rate in Western Indonesia higher. It may be constrained by lower average income and higher average property prices than Eastern Indonesia so that the risk of default is also higher in Western Indonesia which c",1.0,https://csefb.ub.ac.id/index.php/csefb/article/download/57/44
be87397676bb4f77c364fea6fd08e8f0307a5c1a,10.47164/ijngc.v13i5.952,Analysing the Factors Influencing the House Prices and Studying House Price Prediction Methods,2022.0,International Journal of Next-Generation Computing,Purushottam Assudani; Chinmay Wankhede,"Home buyers looking for a new house tend to be very cautious with their budgets and market strategies. Theyalways try to optimise the budget in such a way that it matches their requirements and needs. Therefore predictionof price becomes a very important thing when it comes to planning a budget and there is a need for a predictiontool. This can be achieved by doing data analysis notably Exploratory Data Analysis (EDA) and by developingMachine Learning models. An ideal home that a customer dreams of is something that matches as well as fulfils thecustomer’s requirements but at the same time with an appropriate budget. So instead of going to the real estateagent and paying an additional expense in the form of commission, the same work of suggesting and predictingthe price after analysing large data, is done by various machine learning models in a more efficient manner.Thus the research on house price prediction is of much significance as it caters to two stakeholders in this realestate market, home buyers will have a better understanding of property value and will be helped in the decisionmaking process and hence stand a better stand at negotiating. The other stakeholder i.e. the hom",1.0,https://doi.org/10.47164/ijngc.v13i5.952
28dbdfbf34eec16aaa34379de6b962423aa01e40,10.33168/jliss.2022.0204,"A Study on Information Spillover Effects between the Korean, U. S. and Japanese Housing Markets",2022.0,"Journal of logistics, informatics and service science",Cha Soon; Choi,". Through the recent global financial crisis, there is emerging recognition that a country's housing market shock can affect other countries' housing market volatility. Therefore, this study aims to verify whether price and volatility spillover effects exist between the Korean, U.S., and Japanese housing markets. To this end, this study analyzed the impacts of shocks that occurred in each country on other countries’ housing markets by estimating each country’s returns model of the housing price index from June 1993 until December 2021 using the EGARCH model. The analysis results are as follows: First, the positive asymmetric volatility spillover effect existed due to news shock in the Korean housing market. Second, the negative asymmetric volatility spillover effect was observed due to news shock in the U.S. housing market. Therefore, a leverage effect revealed in the stock market was found. Third, although no asymmetric volatility spillover effect existed between the U.S. and Japanese housing markets, the price spillover effect existed. It was confirmed that information spillover effects were limited in the enormous trends of opening and globalization in the housing market due to ",1.0,http://dx.doi.org/10.33168/jliss.2022.0204
515097927936d6d77ff03b359c8752bcf7686ca7,10.35940/ijeat.f1010.0986s319,A System Dynamics Model of Malaysia House Pricing,2019.0,International Journal of Engineering and Advanced Technology,,"The difficulty to own a house has been a major concern especially among major cities in the world. As the worldwide issue, house prices also have been in the limelight in Malaysia. This is due to the unaffordable prices that tend to escalate over time. The factors that affect house prices are revolving around supply and demand theory either at micro or micro level. There are a lot of factors that found to be correlated as stated from the previous study such as housing characteristics, population, exchange rate, investment, inflation and liquidity. However, there have been shortcomings of study on the liquidity and investment factors toward the house prices. Therefore, this study aims to specifically analyse the influential factors of liquidity and investment toward house prices by using system dynamic approach. The model presumes the liquidity ratios stability and the amount spent on investment in residential properties will affect the house prices significantly. Analysing the trend of rising prices during recent years and forecasting its future development using system dynamic approach is significant, since it can help the authority and society to thoroughly understand on how liqu",1.0,https://doi.org/10.35940/ijeat.f1010.0986s319
d251156a74382116423042e77ed9a324244b3f33,10.22313/reik.2023.21.1.217,"Effect of Microscopic Elementary School Commuting Environment on Housing Price: Focused on Mok-dong and Cheonggye-dong, Hwaseong-si, where Elementary School Students are Concentrated",2023.0,Residential Environment Institute Of Korea,Joo-Sung Kim; Sung-Joon Hong,"Recently, the issue of commuting safety for elementary school students is emerging, and research on this issue is necessary. However, research on the elementary school commuting environment has been mainly focused on accessibility through the straight-line distance between home and school, studies on the effect of microscopic commuting environment on housing prices are insufficient. Therefore, the purpose of this study is to analyze the effect of microscopic elementary school commuting environment on housing price. For this purpose, multiple regression analysis was used with the commuting environment as the independent variables and the housing price per unit area as the dependent variable. The subjects of the study were 324 apartment buildings in 27 apartment complexes in Mok-dong and Cheonggye-dong, Hwaseong-si, which have the highest ratio of elementary school students to the population. For the analysis data, 882 houses for sale registered in Naver real estate platform from July 16, 2021 to July 21, 2021 were used. The analysis results are summarized as follows. First, as the walking distance to the school increased, the housing price decreased. Second, if crossings were necess",1.0,
e03d80e040fcfa4d7726a71678be31e8ea726edd,10.3390/land13122161,"The Influencing Thresholds and Interactive Effects of Urban Green Spaces on Housing Prices: A Case Study of Beijing, China",2024.0,Land,Kexin Cheng; Angshuo Zhan; Haoyuan Luo; Yujing Ma; Zhi-Fang Wang,"As an important part of the urban ecosystem, the value of green spaces (GS) has become increasingly prominent. However, the interaction effect of GS characteristics are still not clear enough. This study explores the premium effect of different types of GS on housing prices (HP) in Beijing. The results show that all types of GS have a significant premium impact on HP, among which there are obvious distance and area thresholds for park GS. The distance range of municipal parks for premium effect is within 2000 m and, the closer to a community, the higher the value. In contrast, community-level parks only have a premium effect within 1500 m, and the most obvious distance is 500–1000 m away from the community. The area of parks has the greatest impact on HP when it is 20–50 ha, and municipal parks larger than 300 ha or community parks smaller than 2 ha do not produce a premium effect. Furthermore, GS surrounding and within communities can serve as substitutes for community-level parks, while municipal parks are irreplaceable and can synergistically influence HP increases with all types of GS. The study offers new findings on the influencing thresholds and interactive effects of GS, pr",1.0,https://www.mdpi.com/2073-445X/13/12/2161/pdf?version=1733934589
8a9d051a7613126b8a3e0280336f2550eb73da44,10.1108/ijtc-02-2024-0047,Residential transformations and real estate market trends associated with tourism activity in urban popular sectors: Ciudad Bolívar in Bogotá,2024.0,International Journal of Tourism Cities,Angélica Camargo Sierra,"
Purpose
This study aims to identify the transformations of the residential landscape and trends in the popular real estate market associated with tourist activity in a low-income neighborhood in Bogotá.


Design/methodology/approach
The methodology was mainly qualitative, although the author analyzed some quantitative data. The author conducted three guided tourist tours in the area and systematically compared photographs taken in 2022 with satellite images in 2019 before starting the tourist activity. To analyze the real estate market, the author uses data on housing offers for sale and rent. Finally, the author conducted semi-structured interviews with tour guides, residents and other key actors in the sector.


Findings
This study identified some dynamics of change in the real estate market, particularly associated with an increase in housing sales and rental prices, as well as a possible effect on the increase of new informal growth processes. The author found a growing transformation of the urban and residential landscape associated with the painting of facades by the local administration and mural painting processes by community organizations. There are evident housing inves",1.0,
72c362e57b15f04ce280b4b5be844eea81106696,10.36448/expert.v15i2.4618,Optimasi Parameter Neural Network untuk Meningkatkan Efisiensi Pembelajaran Model,2025.0,EXPERT Jurnal Manajemen Sistem Informasi dan Teknologi,Ari Kurniawan Saputra; Robby Yuli Endra; E. Erlangga,"-This research was conducted to address the issue of low training stability and inefficiency in Neural Network learning processes caused by the random initialization of parameters such as weights, biases, and learning rate. The objective of this research is to optimize these parameters so that the training process becomes more stable, faster, and more consistent. The Particle Swarm Optimization (PSO) algorithm was employed through seven stages, including data preprocessing, model architecture design, loss computation, weight initialization, optimization of learning rate and initial weights, model training, and performance evaluation. The dataset consists of 10 samples with five input features and one target variable representing house prices. The results show that PSO successfully produced an optimal learning rate of 0.174 and more stable initial weights compared to the baseline model. Evaluation using a confusion matrix demonstrates improved performance over the baseline, which achieved only 60% accuracy, 0.60 precision, 1.00 recall, and an F1-score of 0.75. Overall, PSO optimization has proven effective in enhancing training stability, accelerating convergence, and producing a mo",1.0,https://jurnal.ubl.ac.id/index.php/expert/article/download/4618/3090
44cc6a69608e568400d47a5060f5f149a6b424d9,10.51680/ev.37.2.2,The impact of macroeconomic factors on real estate prices,2024.0,Ekonomski Vjesnik,Anita Peša; Miguel Ángel Latorre Guillem; Marijana Jerić,"Purpose: The paper deals with the comparison of real estate price trends in two European markets such as Spain and Croatia, well-known tourist destinations. Trends in macroeconomic variables in the period from 2013 to 2022 were considered, such as the house price index, overdue mortgage or rent payments, direct investment, production in construction, real GDP per capita, average adjusted wage per employeeand unemployment rate. The aim of this study is to compare real estate prices in these two countries with regard to macroeconomic variables.Methodology: The applied methodology is based on the model averaging technique, which has been used in a few previous similar research studies.Results: The obtained results point to the fact that real estate price movements are strongly negatively influenced by the unemployment rate in both observed countries.Conclusion: The real estate prices in Croatia are strongly negatively influenced by arrears on mortgage or rent payments, while in Spain, they are strongly positively influenced by production in construction. Recommendations for further research refer to research that would cover a wider range of Southeast European countries with common ch",1.0,https://ojs.srce.hr/index.php/ekonomski-vjesnik/article/download/28053/17092
4ef7ce4f14445bf9a33984435f44780b61bbb8ff,10.36948/ijfmr.2023.v05i02.1764,Real Estate Price Predcition System Using Hybrid Lasso Algorithm,2023.0,International Journal For Multidisciplinary Research,Moganarangan; Kishorre Raj; Ashathulla; Devasanjay; Shalinthraj Adhithyan,"House Price prediction system is a website where the user/ buyer can be able to predict the accurate cost of real estate. To analyse the pertinent characteristics and the best models for predicting the price of homes, a literature review is done.. The model will then use the user's data, and the user will be able to view the predicted price of the property they are selling or looking to buy in a matter of seconds. The feature selection process is done with a type of regression called LASSO regression. A kind of linear regression that makes advantage of shrinkage is called LASSO regression. So we are proposing a system that uses Hybrid LASSO Regression algorithm to do the Feature Selection. Hybrid LASSO Regression is hybrid of both LASSO and Ridge Regression. When models exhibit significant levels of multi-collinearity or when you wish to automate specific steps in the model selection process, such as variable selection and parameter elimination, this specific sort of regression is ideally suited. This would greatly help academics and housing developers identify the most important factors that influence home values and recognize the most effective machine learning model to follow wh",1.0,https://doi.org/10.36948/ijfmr.2023.v05i02.1764
bf73d1d45094818d0c1a77e6b22d3690145b5480,10.21924/chss.5.2.2025.97,Towards Housing Policies: Urban Sprawl and Housing Disparities in Jakarta–West Java Growth Corridor,2025.0,Communications in Humanities and Social Sciences,K. A. Putri; I. Hidayat; Muhammad Athallah Alif Bimo Triyogo,"Rapid urbanization in metropolitan cities such as Jakarta has resulted in a growing disparity in house prices between the city center and suburban areas. This present study examines the economic determinants of housing unaffordability, with a focus on Gross Domestic Regional Product (GDRP) and Construction Expensiveness Index (CCI). This study employs a case study of the Jakarta and West Java regions to analyze the impact of these economic indicators on housing affordability and the extending gap between urban and peri-urban areas. The study indicates that higher construction costs in urban centers substantially worsen affordability challenges. Additionally, the study explores the question whether migration to suburban areas represents a significant trend and potential policy measure to address this disparity. The results of the study highlight the interconnectedness of economic growth, construction costs, and affordable housing availability in both urban and suburban contexts. To mitigate housing inequality and foster equitable regional development, this paper proposes policy interventions such as zoning reforms, enhanced public transportation infrastructure, and alternative housi",1.0,https://chss.kipmi.or.id/journal/article/download/97/58
d64de7fa7d1821442ef9373246ebb3eeab4027f6,10.1142/s2345748118500288,"Socio-Spatial Differentiation of Tourist Towns: A Case Study of Huangshui Town, Chongqing",2018.0,Chinese Journal of Urban and Environmental Studies,Yue Li; Jian-Chao Xi,"Tourism urbanization is one of the important patterns of China’s new-type urbanization. Combining Participatory Rural Appraisal (PRA), ArcGIS spatial analysis and high-resolution remote sensing images, the paper takes a case study of Huangshui Town in Chongqing and explores the socio-spatial features such as the spatial functions, spatial culture and social relations of tourist towns. The research results are as follows: (i) From 2007 to 2017, the number of people permanently living in Huangshui Town over the last 10 years increased from 5,923 to 12,699, up by 2.14 times; the urban construction area expanded from 0.15[Formula: see text]km2 to 1.65[Formula: see text]km2, increased by 11 times. (ii) The space of tourist towns has gradually changed from a homogeneous traditional community tied by blood to a heterogeneous social space containing multiple components and dominated by functions such as residence, business, trade and tourism. (iii) This development pattern is centered at indigenous community-based towns, supplemented by second homes of tourism real estate and extended by tourism clusters featuring pan-leisure services. It is of great practical and theoretical value to the ",1.0,
693c070e6cdaf6a78755a8e3c1d814d520eaeae3,10.54097/ng83ed51,The Factors and Strategies of Green Gentrification,2024.0,Highlights in Science Engineering and Technology,Haowei Zhong,"It is clear that urban green spaces provide numerous health, social, and ecological benefits. However, without adequate preventative measures in place, the environmental benefits provided by these projects such as parks, trails, and better accessibility often lead to a rise in property values and significantly higher housing expenses contributing to green gentrification. This paper studies the factors of green gentrification and provides suggestions for solution and strategies to prevent the problems caused by green gentrification. Results show that characteristics of green space and cities’ geography and development are the main factors of green gentrification. This study focuses on the role of the function, scale of green space and park premium and geographical location in terms of characteristics of green space and distance from the city center, population change, economic growth, and city size in terms of cities’ geography and development context. To alleviate the problem caused by gentrification (such as financial pressure, social conflict, and increasing psychological costs), strategies including “just green enough”, multi-stakeholder participation, and fair benefit distribut",1.0,https://drpress.org/ojs/index.php/HSET/article/download/17863/17377
fc847b78bc07a9ffe8e773b4002a2d0a4bf91ff7,10.54097/hbem.v19i.11933,Evaluating the Impact of Airbnb on Social Equality,2023.0,"Highlights in Business, Economics and Management",Zhehao Fu; Hong Zhu; Fengming Pan,"This research paper aims to investigate the development and impact of Airbnb on social, economic, and cultural dimensions, with a focus on its negative impacts on social equality and gentrification. The paper argues that Airbnb gentrifies local communities through the creation of short-term rent gaps, leading to rising housing prices, displacement of long-term users, and social injustice. Using data retrieved from the website of the UK government and Insideairbnb, this study examines the relationship between Airbnb and the well-being of London residents using linear regression. The results show a close correlation between Airbnb and rising housing prices, a fair correlation with decreasing greenery, transportation, and average income levels, and no significant correlation with average life expectancies. The correlation between these variables highlights the negative externalities associated with social justice that have arisen from Airbnb's proliferation. Considering that the growth of Airbnb in London has led to negative consequences for residents' well-being, policymakers should mitigate these impacts while monitoring Airbnb's growth to promote social justice and equitable outcom",1.0,https://drpress.org/ojs/index.php/HBEM/article/download/11933/11618
4e338f7890627c293cd5480dec800ffcec7fef84,10.18778/0208-6018.342.06,The Temporal Stability of Buyers’ Preferences for Property Localization on the Housing Market in Szczecin,2019.0,Acta Universitatis Lodziensis. Folia Oeconomica,Barbara Batóg; I. Foryś,"The housing market investors’ decisions are caused by their subjective expectations. These decisions are to do with financial resources, activity of local developers and buyers’ individual needs connected to the size of their household or their specific habits, as well as a sense of security and prestige of the neighborhood. The localization of a property is one of the most significant determinants of its price because of its spatial constancy. The attributes of a given residential area influence the value of properties and this phenomenon can be observed through intensity of transactions and transaction prices. On the other hand, the neighborhood’s influence changes over time. These changes are sometimes slow but could be very substantial. That is why buyers’ preferences can also change over time. The aim of this research is the analysis of the temporal stability of buyers’ localization preferences on the housing market in Szczecin. The fixed effects model, which allows for the comparison of changes in apartments’ prices in residential areas, has been applied. The data came from notarial deeds, from registers of real estate prices and values concerning transactions on the housing ",1.0,https://czasopisma.uni.lodz.pl/foe/article/download/3357/5198
a090a9e2f62d50d7c4f9b35da3141a7a1e038603,10.4108/eai.18-11-2022.2327138,Influencing Factors of California Housing Prices in 1990: a Multiple Linear Regression Analysis,2023.0,"Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022, Nanjing, China",Yitan Hao; Luhan Zhuang; Zitao Ying; Juncheng Zhai,"— In 1990, it was a hot topic to study the housing prices of blocks in California. That was mainly because of the recession period of the U.S. economy in 1990, which affected consumption and personal income and impacted the real estate market. The purpose of the study is to find factors that affect the housing price for blocks in California in 1990 with the consideration of properties of the house itself, the income of buyers, and the geographic surroundings. The paper uses the housing price impact theory and multiple linear regression method to study the influencing factors of the California housing price in 1990. This research concludes that median house value is positively correlated with median income, median house age, total bedrooms, households, and distance to San Diego, while negatively correlated with total rooms, population, distance to the coast, and Los Angeles. In addition, there is no statistically significant relationship between the median house value and distance to San Jose. In fact, it gives very specific results about the impact of each specific factor involved in our model. However, the general ideas like the impact of the whole dwelling structures do not invol",1.0,http://eudl.eu/pdf/10.4108/eai.18-11-2022.2327138
cc28b911caa807254240448f06edd4a66e8b8665,10.1080/1331677x.2021.2019076,Comparisons of housing price risks between first-time buyer and former owner-occupier markets in England,2021.0,Economic Research-Ekonomska Istraživanja,I. Tsai; Wen-Kai Wang,"Abstract This study employs housing data from the nine main regions of England and from overall England to compare the volatility risks of housing prices for the first-time buyer (FTB) and former owner-occupier (FOO) markets. This study collects data from February 2012 to March 2020 to assess the volatility characteristics of housing prices in these two markets in each region of England. Additionally, the effect of interest rate shocks and the wealth effect of the stock market on these two markets are observed. The results reveal that for most regions, the FTB market has higher housing price volatility risks compared with the FOO market. Additionally, the FTB market is more easily affected by interest rate shocks and the wealth effect. In contrast to FOOs, FTBs are generally younger and less wealthy. If FTBs are facing a market with higher housing price risk, especially when the overall economy is affected by a shock (such as the European Debt Crisis or the COVID-19 pandemic), the response of the FTB housing market is greater; then the government must pay special attention when implementing policies that interfere with the market since these policies would particularly affect the w",1.0,https://doi.org/10.1080/1331677x.2021.2019076
59996b186a37311460696472ae433850e2f1b2ae,10.21447/jusre.2022.13.2.21,Determinants of the Housing Investment during Periods of Increasing Housing Price: Focusing on “Gap Speculation”,2022.0,The Korean Association of Urban Policies,Sungwon Lee,"The aim of the study is to examine the determinants of housing gap speculation from 2017 to 2020. Housing price have steeply increased last 5 years in Korea. The median apartment sale prices in the Seoul Metropolitan Areas (SMAs) increased by about 60.5% from about 370 million won in 2017 to about 590 million won in 2021. Housing policy makers believe that young people who cannot afford to buy a house with their own income and assets entered the housing market through gap speculation, and thereby housing prices skyrocketed with increasing housing effective demand. Therefore, various demand-side strategies have been applied to curb speculative demand. Extensive financial borrowing restrictions and increase in tax, nevertheless, have not stopped the rise in housing prices. This study focuses on what gap speculation is and what characteristics gap speculators have. Theoretically, it can be conceptualized as buying a house on a cheonsei contract - a kind of rental agreement, but a unique Korean way to rent a house with only a long-term and large deposit - for the purpose of capital gains without a plan to live there. From the empirical study, we can find several things as follows. Cont",1.0,
ade6ed1d19786ee39dbd2cd53c21e16e7912f0c1,10.30902/jrea.2024.10.2.23,"Analysis of Momentum Effects in the Seoul Apartment
 Market",2024.0,Journal of Real Estate Analysis,Yun-Ho Choi,"This study analyzes momentum effects in the indices of apartment sale prices for 25 districts in Seoul with momentum investment strategies from Jegadeesh and Titman. The analysis employs housing price trend data from the Korea Real Estate Board and Kookmin Bank. To reflect the long-term characteristics of the real estate market, the study sets the portfolio formation period (J) and the holding period (K) as 3, 6, 9, 12, 24, 36 and 60 months. The results show a significant momentum effect in the short term, but the effect either fades or reverses over the long term. With the capital asset pricing model (CAPM) model to verify whether the short-term momentum effect results from systematic risk or market inefficiency, the study finds conflicting conclusions depending on two data. Nevertheless, they suggest market inefficiency as the alpha for the winner portfolio is positive, while the alpha for the loser portfolio is negative. Since the indices of apartment sale prices may not fully reflect actual price changes, the actual momentum effect could be stronger. However, practical limitations such as capital gains tax and brokerage fee may constrain the implementation of short-term momentu",1.0,http://www.ejrea.org/download/download_pdf?pid=jrea-10-2-23
a16279862032dff3238dd72d8e2f29e3e2c8435e,10.29173/topo35,"Differences in the effect of homeownership on health status across health regions in Alberta, 2011-2012",2020.0,,Meryn Severson,"The housing we live in - from the type and the location to our homeownership status - impacts our health status. Housing is one of the most central environments individuals live in, and as a socio-economic determinant of health, has disproportionate impact on certain groups. Previous research indicates that homeowners tend to have better health than renters. However, this relationship changes when living in unaffordable housing. Organizations have issued numerous warnings about rising unaffordability, debt, and home prices in Canada. In this paper, I focus on the effects of homeownership status in the five different health regions in Alberta, stratified by housing affordability. Using Statistics Canada’s Canadian Community Health Survey from 2011 - 2012 in a logistic multivariate regression, I find that homeownership is positively associated with self-reported good or better health status, but that the association was smaller in less affordable regions. This suggests living in regions that are less affordable dampens the health benefits of homeownership. These findings also support the the idea that homeownership is more than a proxy for socioeconomic status and has its own effects",1.0,https://journals.library.ualberta.ca/topophilia/index.php/topophilia/article/download/35/26
c7b6f4e2f6a7f59f7730fb5b1f9d4b475ac104b6,10.1145/3705328.3748136,Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing Search,2025.0,ACM Conference on Recommender Systems,Ivo Silva; Pedro F. Nogueira; Guilherme G. Bonaldo,"QuintoAndar Group is Latin America’s largest housing platform, revolutionizing property rentals and sales. Headquartered in Brazil, it simplifies the housing process by eliminating paperwork and enhancing accessibility for tenants, buyers, and landlords. With thousands of houses available for each city, users struggle to find the ideal home. In this context, location plays a pivotal role, as it significantly influences property value, access to amenities, and life quality. A great location can make even a modest home highly desirable. Therefore, incorporating location into recommendations is essential for their effectiveness. We propose a geo-aware embedding framework to address sparsity and spatial nuances in housing recommendations on digital rental platforms. Our approach integrates an hierarchical H3 [5] grid at multiple levels into a two-tower neural architecture. We compare our method with a traditional matrix factorization baseline and a single-resolution variant using interaction data from our platform. Embedding specific evaluation reveals richer and more balanced embedding representations, while offline ranking simulations demonstrate a substantial uplift in recommendatio",1.0,https://arxiv.org/pdf/2510.01196
b8bb4f416318e6d4d89cc89b686bd16d95048ac3,10.5539/ijef.v10n8p43,The Commodity Housing Price Analysis Model--Take Haikou (China) as an Example,2018.0,International Journal of Economics and Finance,Bing-Qian Liu; Xiao-Yan Cao; Qi-Fan Yang; Yuan-Biao Zhang,"In recent decade years, the real-estate industry in China has achieved unprecedented development. Correspondingly, the rapid rise in house prices has led the government to introduce a series of macro-control policies. Based on the main regulatory mechanism of the purchase restriction policy, we take Haikou as an example to analysis the probable influence on housing price. We first select indicators from three aspects: supply, demand, and macroeconomic environment, and then establish a gray correlation model to extract the key factors of strong correlation, that is, real- estate investment, CPI, residential housing construction area, residential housing completion area. Moreover, we establish a multiple linear regression model based on GM (1, n) to obtain the multi-function relationship between commercial housing prices and these four key indicators. After that, we establish a population- purchases demand function model to predict the price of commercial housing in the coming year after introducing the purchase restriction policy. More significantly, we conclude that the purchase restriction policy can effectively regulate housing prices in the short term, but the long-term effect i",1.0,https://doi.org/10.5539/ijef.v10n8p43
ec7b52328bae99bbb22d4e8a7dd5b9a8c52e95ad,10.54254/2755-2721/29/20231181,Machine learning techniques for predicting home rental prices in India,2023.0,Applied and Computational Engineering,P. Jayadharshini; S. Santhiya; S. Keerthika; N. Abinaya; S. Priyanka,"Predicting the selling price of houses has become increasingly crucial as land and housing prices rise annually. This task is particularly challenging for metropolitan areas like Chennai and Bangalore. Therefore, there is a growing demand for an easier and more effective approach to forecast house rental prices, ensuring future generations have access to reliable predictions. Several key factors, such as the house's location and area, significantly influence rental prices. In this paper, a dataset comprising ten similar crucial features is utilized. The model is developed using a Python library, where the data is preprocessed and prepared to ensure cleanliness for constructing the model. Various machine learning algorithms, including Random Forest, Linear Regression, Decision Tree Regression, and Gradient Boosting, are employed. Through feature extraction, it is determined that area and property type are the most important features that significantly impact rental prices. Among the techniques used, gradient boosting yields the most satisfactory predictive results for rent based on evaluation metrics like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-Squared Metri",1.0,https://www.ewadirect.com/proceedings/ace/article/view/9326/pdf
3ff76d95b8a6880a949651b0091fe38ecc60876f,10.3390/urbansci10020116,"Assimilation or Segregation? Evolutionary Trajectories and Driving Forces of Chinese Immigrant Residential Concentration in Seoul, South Korea",2026.0,Urban Science,Hanbin Wei; Yi Zheng; Xiao-Lei Sang; Mengru Zhou; Sunju Kang,"The spatial distribution of immigrants and associated patterns of residential segregation and integration can manifest not only at the metropolitan scale but also at finer micro-spatial resolutions, reflecting the interaction between path dependence and structural reconfiguration. This article examines the micro-spatial residential patterns of Chinese immigrants in Seoul under institutional and market constraints. Using a Spatial Durbin Model and Multiscale Geographically Weighted Regression, it shows that from 2011 to 2025, immigrant settlements shifted from a monocentric pattern to a polycentric, functionally differentiated, and networked structure. While overall spatial embeddedness is high and segregation remains low, traditional cores such as Guro–Daerim persist. Selective clustering is shaped by path-dependent migrant networks, urban redevelopment policies, and intra-group differentiation, while infrastructure homogenization renders transportation accessibility a background condition. The findings support segmented assimilation theory in high-density East Asian cities and underscore the importance of incorporating immigrant needs into urban policy to promote inclusive integra",1.0,https://doi.org/10.3390/urbansci10020116
bda7e22d4e2610c5e517c48b14c12014a9406cac,10.32628/ijsrst523103134,A Novel Model for House Price Prediction with Machine Learning Techniques,2023.0,International Journal of Scientific Research in Science and Technology,Harshada S. Belsare; K. Warkar,"In this paper, we are going to use machine learning algorithms for house price prediction. House prices increases drastically every year, so we felt a need for a system that will predict house prices in the future. Due to a lack of knowledge of property assets people cannot guess the accurate price of houses. Therefore, we felt a need for a model that will predict an accurate house price. So, the main aim of our project is to predict the accurate price of the house without any loss. This survey also deals with a comparative analysis of the results of the algorithms used and the model with the highest accuracy and minimum error rate will be implemented. For the choice of prediction ways, we tend to compare and explore numerous prediction ways. We tend to utilize Linear and random forest regression as our model attributable to its liable and probabilistic methodology on model Choice. Our result exhibits that approach to the problem ought to achieve success and has the flexibility to predictions that will be compared to different house price prediction models. We have a proclivity to propose a house price prediction model to hold up a customer to estimate the proper valuation of a hou",1.0,https://ijsrst.com/paper/11261.pdf
f921b44c7e05451da0abd8af2356c3fde4c9cdac,10.24957/hsr.2019.27.4.89,Changes in the Comovement of House Price and Jeonse Price in the Korean Housing Market Considering the Macroeconomic Factors,2019.0,,H. Jang; Byungkwon Lim; Hyung Keun Kim,"This study investigates the change of comovement between the house price and the rent price (hereafter Jeonse price). The methodology used in this study is the SVAR (1) model that assumes the US federal funds rate (FFR) to be block exogenous, and dynamically examines the comovement of the house price and the Jeonse price in the Korean housing market from 2004 to 2018. The main results are as follows. First, we found that the increase in house price is related to the increase in the industrial production index (IPI) and Jeonse price. We also found that the increase in Jeonse price is associated with the increase in the interest rate, IPI, consumer price index (CPI), and house price. Meanwhile, we find that the house price decreases in the 1% upward pressure of domestic and foreign interest rate shock. However, Jeonse price is only influenced by the domestic interest rate shock. It is noted that the increase of IPI and CPI commonly affects both the house price and the Jeonse price. Second, we carried out a rolling regression analysis in order to identify the dynamic casual relationship. We find that the impact of house price and Jeonse price from the IPI and CPI decrease as the inter",1.0,
84034faff74c1615cf1da620804dd2b3dac85333,10.1155/2022/2948352,"Classification of Housing Submarkets considering Human Preference: A Case Study in Shenyang, China",2022.0,Mathematical Problems in Engineering,Xin-Rui Liu; Rong-Fei Guo; Guo-Ping Lei; Na Liu,"Housing submarket is affected by the quality of public facilities in neighborhood. In this article, a three-step framework is proposed to classify the housing market in Shenyang city, China. The saliences of point of interest, POI, are used to quantify the degree of activities using social media data and merged into the model of density-field hotspot detector to extract high-quality of urban facilities, aiming at exploring the influences on the housing price. The multiscale geographically weighted regression model, MGWR, is applied to investigate relations between the housing price and attribute variables geographically and to identify multiscale impacts on the housing price with flexible bandwidths. The spatial “k”luster analysis by the tree edge removal method, SKATER, is applied to delimit urban housing submarkets based on the MGWR betas, where ten groups of the housing submarkets are recognized. The proposed classification method captures multiscale spatial heterogeneity of the housing price and predominant influencing factors for each housing submarket in Shenyang, China. It also offers suggestions for urban planners to restrain the unbalanced development of the housing market",1.0,https://downloads.hindawi.com/journals/mpe/2022/2948352.pdf
e1956f8e931ad44da89538d9ee744bb53b1fe0cd,10.1088/1755-1315/128/1/012067,Research on the factors influencing the price of commercial housing based on support vector machine (SVM),2018.0,,Xiaoyang Zhong; H. Ren; Jing-Xin Gao,"With the gradual maturity of the real estate market in China, urban housing prices are also better able to reflect changes in market demand and the commodity property of commercial housing has become more and more obvious. Many scholars in our country have made a lot of research on the factors that affect the price of commercial housing in the city and the number of related research papers increased rapidly. These scholars’ research results provide valuable wealth to solve the problem of urban housing price changes in our country. However, due to the huge amount of literature, the vast amount of information is submerged in the library and cannot be fully utilized. Text mining technology has been widely concerned and developed in the field of Humanities and Social Sciences in recent years. But through the text mining technology to obtain the influence factors on the price of urban commercial housing is still relatively rare. In this paper, the research results of the existing scholars were excavated by text mining algorithm based on support vector machine in order to further make full use of the current research results and to provide a reference for stabilizing housing prices.",1.0,https://iopscience.iop.org/article/10.1088/1755-1315/128/1/012067/pdf
389784401469f3cec8da4c2b856ddd829015f4b2,10.3390/land12030578,The Costs of Agglomeration: Misallocation of Credit in Chinese Cities,2023.0,Land,Lu Liu; Yu Tian; Hai-Quan Chen,"In comparison to agglomeration economies, the negative externalities of agglomeration have received less attention. Based on the punishment data of illegal loans from Chinese bank branches, this paper focuses on the relationship between density and credit misallocation to enrich the study of the negative externalities of agglomeration. After controlling for city size and a variety of other possible factors, the empirical results suggest that increased density leads to increased credit misallocation, and this result passes a series of robustness tests, including IV estimation. Agglomeration causes credit misallocation primarily through rising house prices, which causes financial institutions to favor housing-related loans. During this process, the government’s reliance on land finance causes higher house prices and, as a result, more severe misallocation. However, agglomeration does not always lead to an increase in credit misallocation. There are also cases where agglomeration does not lead to an increase in credit misallocation, which suggests that we should pay attention to differences in key characteristics between cities when studying the externalities of agglomeration.",1.0,https://www.mdpi.com/2073-445X/12/3/578/pdf?version=1677566765
c596173442bd65b7157b5598f010579f3a3fe6af,10.33119/knop.2023.67.1.8,Housing prices during the COVID-19 pandemic: insights from Sweden,2023.0,Kwartalnik Nauk o Przedsiębiorstwie,C. André; Thomas Chalaux,"The COVID-19 pandemic triggered a major global economic recession, to which policymakers around the world responded with massive fiscal and monetary support. While housing prices generally fall during economic downturns, they have risen during the pandemic in all OECD countries. A number of factors may have contributed, including expansionary monetary policy, the lifting of some macro-prudential constraints and a shift in housing preferences. This paper uses monthly data to examine the behaviour of real house and flat prices during the pandemic in Sweden, at the national level and in the three biggest cities. While a model containing usual determinants of housing prices tracked price developments well before the pandemic, it underestimates house prices and generally overestimates flat prices in the pandemic period. This suggests a preference shift from flats towards houses, which is consistent with findings from the recent literature on other countries. The introduction of mortgage amortisation requirements in 2016 and 2018 is estimated to have lowered housing prices. However, their lifting during the pandemic seems to have had a relatively minor effect on housing prices.",1.0,https://econjournals.sgh.waw.pl/KNoP/article/download/3319/2974
2978e64c8b75224a83ddd496a45e744aa32769d7,10.1109/icwite64848.2025.11307001,House Price Prediction: a Comparative Study of Machine Learning Ensemble Techniques and Deep Learning Model,2025.0,"2025 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE)",Melissa Dsilva; Vineet Kadam; H. Kadam; Himani Deshpande,"House price prediction using machine learning has gained substantial attention in recent years due to advancements in algorithms, as well as the increasing availability of real estate data. The conventional approaches of valuation cannot properly reflect the dynamic and complex character of real estate markets, thus the implementation of machine learning (ML) models for higher precision. This study revolves around house price prediction: Random Forest, XGBoost, LightGBM, and ensemble learning approaches. The dataset contains 29,451 properties and has considered 12 vital features like location, area, number of bedrooms, various accessibility factors, etc. The Stacked Model (Random Forest + XGBoost + LightGBM) gives the best accuracy score with an MAE of 2.9511 lakhs and the $\mathbf{R}^{2}$ score of 0.9785. In addition to this, an Enhanced Stacked Model with SVR, MLP, and KNN was developed but performed worse than the simpler stacked model producing MAE of 3.1673 Lakhs and $\mathbf{R}^{\mathbf{2}}$ of 0.9768. A Feedforward Neural Network (FNN) was implemented, yielding a mean absolute error (MAE) of 16.7667 lakhs and an $\mathbf{R}^{\mathbf{2}}$ score of 0.9371. This suggests that i",1.0,
2f61b315ee49e0c729d63ab20c92f944a341b7c8,10.1111/tgis.70239,Constructing the Street Environment Composite Metric for Property Valuation: A Hybrid Approach Using Semantic Segmentation and Spatial Statistics,2026.0,Trans. GIS,Yi-Ni Meng; Jian Yin; Hui-Xiao Wang,"Quantifying the synergistic effects of urban environmental features remains a challenge in geocomputation and urban modeling. Traditional studies often treat the Green View Index (GVI) and Sky View Factor (SVF) as independent variables, overlooking their complex interaction in determining environmental quality. This study proposes a hybrid computational framework coupling semantic segmentation with spatial statistics to construct the Street Environment Composite Metric (SECM) for property valuation. Utilizing the DeepLabV3+ model, we processed massive unstructured Baidu Street View (BSV) imagery to extract high‐precision semantic features. To enhance spatial modeling granularity and mitigate the Modifiable Areal Unit Problem (MAUP), we implemented a fine‐grained segmentation algorithm for street vectors. These computed metrics were then integrated into a Multiscale Geographically Weighted Regression (MGWR) model to reveal the spatial non‐stationarity of housing price determinants in Shanghai's Xuhui District. Computational results demonstrate that the SECM provides a superior fit for capturing the non‐linear interactions between greenery and spatial openness compared to single‐vari",1.0,
bbc85c0376ef4c6974813a189124a40708684478,10.3846/ijspm.2023.19935,PRICE CHANGES OF REPEAT-SALES HOUSES IN KAOHSIUNG CITY: ANALYSES BASED ON HIERARCHICAL LINEAR GROWTH MODELS,2023.0,International Journal of Strategic Property Management,Chun-Chang Lee; Yu-Chen Wang; Chih-Min Liang; Zheng Yu,"This study adopts the hierarchical linear growth modeling approach to analyze the differences in the changes of repeat-sales house prices in Kaohsiung City from 2012 to 2020. The Level 1 time-varying factors include house age and the time of repeat-sales; the Level 2 factors include house attributes such as house area, house type, and house location. Based on the results of the null model, the estimated variance is 0.42816, with a 1% level of significance. This shows that significant differences exist in the mean repeat-sales prices between houses. The interclass correlation coefficient is 91.65%, showing that the interclass variation and intraclass variation of the mean repeat-sales prices are 91.65% and 8.35%, respectively. The estimation results of the non-randomly varying slope model indicate that the sales time and sales time squared significantly affect repeat-sales prices. The annual growth rate and quadratic growth of sales prices do not differ by house type (luxury condominiums and apartment buildings) but are affected by house area and house location. The effect of house age on repeat-sales prices is moderated by house area, house type, and house location.",1.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/19935/11929
87c74ee4b2aeb5165af9342a5d3b2e1122f0d25b,10.38100/jhuf.2025.10.2.5,"Analyzing the short- and long-term effects of housing supply
 on jeonse price fluctuations and their spatial spillovers",2025.0,Journal of Housing and Urban Finance,J. Choi; H. Chang,"This study empirically analyzes the impact of housing demolition and new completions on apartment rental prices across 25 districts in Seoul. To quantify the temporal and scale-dependent effects of supply shocks, panel regression, panel autoregressive distributed lag, and spatial econometric models were employed, allowing for a multidimensional assessment of the relationship between supply and rental prices. The results showed that demolition leads to short-term increases in rental prices, with stronger effects in large-scale cases. By contrast, new completions contribute to stabilizing rental prices in the long run, with the rental market adjusting relatively quickly to absorb supply shocks. Moreover, the analysis reveals that the effects of demolition and competitions are not confined to individual districts; rental price fluctuations in neighboring districts also influence local markets, indicating strong spatial interdependence across districts. These findings highlight the importance of policy responses beyond short-term measures, emphasizing the need for long-term housing supply strategies and spatially coordinated approaches to ensure market stability.",1.0,http://www.ejhuf.org/download/download_pdf?pid=jhuf-10-2-5
e6446985cde76fcd6e8a310458e6c8469fe46372,10.54097/hset.v44i.7355,Guangzhou Housing Price Prediction Based On Machine Learning Regression Models,2023.0,Highlights in Science Engineering and Technology,Ziwen Huang; Kexin Li; Chuming Wang; Jiazhi Wang,"Nowadays, the fluctuation of housing prices is a key concern of homeowners, the real estate market and the government. The researchers found that it was possible to predict house prices accurately by analyzing relevant attributes and using the most effective models. However, due to the impact of the COVID-19 pandemic, there is still a research gap on which model is more suitable for predicting housing prices after the outbreak of COVID-19. Therefore, this study collected the latest real estate data of Guangzhou in China through the web crawler, trained the random forest regression model with collected data, and obtained a model that could predict the housing price by inputting corresponding attributes. The MSE of the trained regression model is 2802, MAE is 534, and the determination coefficient (R²_score) is 0.89, while the MSE of the trained XGBoost regression model is 3108, MAE is 643, and the determination coefficient (R²_score) is 0.87.The random forest regression model has been shown to be more accurate at predicting Guangzhou house prices after the COVID-19 outbreak. Our paper has great potential in house price prediction under the pandemic situation.",1.0,https://drpress.org/ojs/index.php/HSET/article/download/7355/7162
2a5e3fbeddc0ce665850613799e472a784b05070,10.18778/0867-5856.32.2.06,Rail trails and housing values: A longitudinal study,2022.0,Turyzm/Tourism,Alina Gross; R. Bristow,"Greenways are recognized as an essential tool to build sustainable communities. An example in the United States are the rail trails where decommissioned rail corridors are being improved to provide the benefits of non-motorized transportation systems for recreation and exercise, and the daily commute to school, work and shopping. Yet despite these benefits some still argue the presence of the rail trail as a NIMBY (Not In My Back Yard). To address this argument, we calculate the economic benefit to homeowners living in proximity to rail trails over a ten year period to observe the dramatic changes in value over time. It was hypothesized that there would be a change in housing values based on proximity to greenways. This article will begin with an overview and a literature review on research related to greenways and home values, describe the context of this particular case study in the Pioneer Valley of Western Massachusetts, United States, provide an overview of the methods employed for the calculations and analysis, and conclude with some reflections and the potential limitations that should be considered for undertaking future research on this topic.",1.0,https://czasopisma.uni.lodz.pl/tourism/article/download/13265/16812
4d0303ee931ed16d4c65f4c3bcba92abdf0be729,10.2478/crebss-2022-0003,The influence of recurrent property income and expenditure on house prices in European Union countries: Evidence from a panel model,2022.0,"Croatian Review of Economic, Business and Social Statistics",Julijana Angelovska; A. Č. Časni,"Abstract Land value capture can be defined as a policy approach that allows communities to restore and reinvest land value increases that result from public investment and other government actions. For that reason, public action should generate public benefit. The recurrent property tax, one of many tools for land value capture, is the foundation of a stable, enduring revenue source that supports the provision of essential housing and amenities services. This empirical paper aims to examine the influence of recurrent property tax income, and general government spending on housing and community amenities on house prices. To assess the hypothesized direction of the effects, yearly data structured in a balanced panel on a sample of 26 European Union economies from 2010 to 2019 was used. Fixed effects regression model with Driscoll and Kraay standard errors was employed and the results confirmed a negative but statistically insignificant effect of increased property tax revenue on house prices, while increased expenditure on housing and amenities confirmed a positive and statistically significant effect on house prices dynamics in European Union countries.",1.0,https://sciendo.com/pdf/10.2478/crebss-2022-0003
cc861b3ff0cd2c0f8f8bfe2549b8c9c5aa95432a,10.69849/revistaft/ch10202101200839,URBAN REVITALIZATION: INTEGRATING HISTORICAL HERITAGE AND LEISURE SPACES,2021.0,Revista ft,C. E. D. de Oliveira,"The revitalization of historic urban centers is crucial for preserving the cultural and architectural heritage of cities, while also meeting the contemporary needs of their residents. An essential strategy to achieve these goals is the integration of leisure spaces, such as urban parks, which not only promote social interaction and the well-being of inhabitants but also act as catalysts for the social and economic regeneration of historic areas. These spaces make urban centers more accessible and attractive, improving the quality of life and offering environmental benefits such as reducing the urban heat island effect and increasing biodiversity.
Additionally, recent studies highlight the growing importance of leisure amenities for population growth, attracting skilled professionals, and enhancing property values. Creating pleasant urban environments with leisure infrastructure is considered fundamental for the sustainable development of cities. However, economic growth brings challenges, such as gentrification and the displacement of minority communities, pointing to the need for public policies that ensure social inclusion and preserve the cultural character of revitalized areas.",1.0,
2924f21d7d62f40497b2af3354e9938d715420fe,10.2478/remav-2024-0001,Social Assessment of the Effect of Construction of the Southern Warsaw Ring Road on the Quality of Life and Residential Real Estate Market in the Wilanów District,2024.0,Real Estate Management and Valuation,Natalia Sajnóg; Monika Wierzchoś; K. Sobolewska-Mikulska,"Abstract Every road investment has both positive and negative consequences in spatial, economic, environmental, and social terms. The objective of the article is the social assessment of the effect of construction of the Southern Warsaw Ring Road (WRR) on: (i) the quality of life of the local community, and (ii) the residential real estate market. The analyses employed the method of quantitative analysis with the application of the survey technique. The questionnaire was made publicly available to residents of the Wilanów district in Warsaw. Results obtained in the scope of the study render decision making models of respondents depending on the distance of the place of residence from the WRR. The results reveal that responses given by the group of respondents living nearer the ring road are the most variable, particularly in the context of assessing the effect of the investment on the quality of life, including nuisance related to the express road and plans to change the place of residence. The same group of respondents provided the highest share of responses supporting the claim that housing prices increased due to the construction of the ring road.",1.0,https://sciendo.com/pdf/10.2478/remav-2024-0001
70c25ec0aad99bd59c2df0eacba1d1f3cef53c2c,10.3390/land13050575,Extracting Features from Satellite Imagery to Understand the Size and Scale of Housing Sub-Markets in Madrid,2024.0,Land,G. Kenyon; Dani Arribas-Bel; Caitlin Robinson,"The following paper proposes a novel machine learning approach to the segmentation of urban housing markets. We extract features from globally available satellite imagery using an unsupervised machine learning model called MOSAIKS, and apply a k-means clustering algorithm to the extracted features to identify sub-markets at multiple intra-urban scales within a case study of Madrid (Spain). To systematically explore scale effects on the resulting clusters, the analysis is repeated with varying sizes of satellite image patches. We assess the resulting clusters across scales using several internal cluster-evaluation metrics. Additionally, we use data from online listings portal Idealista to measure the homogeneity of housing prices within the clusters, to understand how well sub-markets can be differentiated by the image features. This paper evaluates the strengths and weakness of the method to identify urban housing sub-markets, a task which is important for planners and policy makers and is often limited by a lack of data. We conclude that the approach seems useful to divide large urban housing markets according to different attributes and scales.",1.0,https://www.mdpi.com/2073-445X/13/5/575/pdf?version=1714135812
cf6dbb072821792ae38236a6cb3980e99b9cb8b2,10.3389/frsc.2026.1793407,"Housing affordability and the teacher workforce: the case of Hobart, Australia",2026.0,Frontiers in Sustainable Cities,Nicola Kidston; Scott Eacott; Zih-Hong Lin,"Education systems globally are experiencing teacher shortages. While substantial policy and scholarly attention have been directed at supply and demand and working conditions for teachers, less attention has been paid to ensuring that teachers can afford to live near the schools where they are most needed. Housing issues, both scarcity and unaffordability, are significantly impacting teachers with cascading effect throughout school systems. Put simply, teaching salaries have been unable to keep up with housing and transportation costs, especially in cities. Drawing on the case study of the Australian city of Hobart (capital of the state of Tasmania), this paper reports on a study integrating housing and education data to establish the impact of costs. Findings indicate a negative association between housing prices and commuting distance. The results do not imply that rising housing prices are universally beneficial for teachers. Rather, they highlight teacher workforce challenges as a spatial problem. Addressing teacher shortages requires attention not only to supply and demand and working conditions, but also the proximal housing of teachers.",1.0,https://doi.org/10.3389/frsc.2026.1793407
6f837eb072914b1501bb722a2c224c23e93fcfc0,10.3846/ijspm.2024.21675,"HOUSING PRICE, FAMILY STRUCTURES AND HOUSEHOLD CONSUMPTION: EMPIRICAL EVIDENCE OF TAIWAN",2024.0,International Journal of Strategic Property Management,Chih-Hsing Hung; Yen-Hung Chen; Chung-Chieh Cheng; Shyh-Weir Tzang,"The housing price affects the allocation of family budget on consumption. We use regression analysis to confirm whether Taiwan’s housing prices have a significant influence on household consumption based on the questionnaire survey on household income and expenditure in 2018. We find that housing prices have the greatest impact on household consumption, possibly because housing accounts for the largest proportion of household assets, implying a significant wealth effect. In addition, the impact of interest of deposit on consumption is also found to be greater than that of household income as it is related to the level of total assets. The housing prices for single-person household with female gender have a negative and significant impact on household consumption. Finally, housing prices are found to have a significantly negative effect on non-consumption and current transfer expenditure. The possible reason is that the households who hold the house believe that the appreciation of housing price can provide more protection for the family from suffering from possible economic risks in the future and thus crowd out non-consumption expenditures.",1.0,http://dx.doi.org/10.3846/ijspm.2024.21675
d0498e1331aede705a6bca174d284b6ac4e9ed72,10.1108/jerer-09-2020-0051,Real estate development in the city of Athens during the financial crisis,2021.0,,Maria Nikitidou; F. Archontakis; Athanasios O. Tagkalakis,"
Purpose
This study aims to determine how the prices of residential properties in the Greek real estate sector are affected by their structural characteristics and by the prevailing economic factors during recession.


Design/methodology/approach
Based on 13,835 valuation reports for the city of Athens, covering a period of 11 years (2006–2016), this study develops a series of econometric models, taking into account both structural characteristics of the property market and the macroeconomic relevant variables. Finally, the city of Athens is divided into sub-regions and the different effects of the structural factors in each area are investigated via spatial analysis confirming the validity of the baseline model.


Findings
Findings show that the size, age, level, parking and storage space can explain the property price movements. Moreover, the authors find evidence that it is primarily house demand variables (e.g. the annual average wage, the unemployment rate, the user cost of capital, financing constraints and expectations about the future course of the house market) that affect house prices in a statistically significant manner and with the correct sign. Finally, using a differ",1.0,
feb5c9e07f5478abebc4bec2ac818b953238f35a,10.15405/epsbs.2016.08.61,Equilibrium Price Application Modelling for Affordable Housing Market in Malaysia,2016.0,,Fazilah Ramli; R. Zainal; Maimunah Ali,"The main purpose of this study is to propose the equilibrium price modelling for affordable housing in Malaysia. This paper examines whether the equilibrium price of affordable housing is strongly related with any affordable housing demand factors (house price, housing physical state, monetary status, population changes, location, and infrastructure amenities provided) and affordable housing supply factors (house price, cost of construction, land availability, population changes, location, and government interventions). The empirical data of housing prices are collected from Valuation and Property Services Department of the Ministry of Finance Malaysia from 1995 to 2015. Regression analysis is conducted to see the strength of relationship between the affordable housing demand and supply factors with the affordable housing equilibrium price. The paper is useful for buyer and private developers to know which factors lead to equilibrium price of affordable housing. This paper can serve as a guide for the government in controlling and stabilizing the affordable housing price. © 2016 Published by Future Academy www.FutureAcademy.org.uk",1.0,https://doi.org/10.15405/epsbs.2016.08.61
c73f2d05039f1812b77b6d6ab09f1fc36d1217f6,10.1109/iatmsi64286.2025.10985703,An Automated Property Pricing System using Machine Learning,2025.0,2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI),A. Dubey; Arvind Lovewanshi; Manikant Thakur; Namrata Dhanda; K. Gupta,"The use of data mining in today's real estate industry is increasingly widespread, particularly for predicting property values, identifying key housing features, and extracting insights from raw data. This technology plays a critical role by providing information that aids various stakeholders in the real estate market. Fluctuations in property values can cause anxiety for homeowners and industry professionals alike, and the rising demand for buying and renting homes, driven by urbanization, further intensifies the need for accurate property valuations. As a result, developing more efficient methods to estimate home prices that reflect current market trends has become crucial. This study focuses on evaluating various supervised machine learning regressors for house price prediction. Algorithms such as Extra Trees Regressor, K-Nearest Neighbors (KNN), Decision Tree Regressor, Bagging Regressor, and Linear and Logistic Regression are tested to determine their efficacy in predicting home values. A comparative analysis is performed among these algorithms to identify which one best fits the dataset and model. Ultimately, the most effective algorithm is selected to create a prototype for",1.0,
a4ac7b936f71017ed4364dfb9ade3f89f293bea5,10.1016/j.drugalcdep.2026.113044,"""Not in my backyard"": The impact of recovery residences on property values.",2026.0,Drug and Alcohol Dependence,Ryan Ramphul; Sheryl A. McCurdy; Jooyeon Lee; Yanchen Liu; Yixiao Chen; Serena A. Rodriguez; Kathryn R. Gallardo; Sreelatha Akkala; Hadiza Theyra-Enias; J. M. Wilkerson,"BACKGROUND
In 2023, an estimated 39.6 million adults aged ≥ 18 years had a substance use disorder (SUD) and 94.7 % did not seek treatment. Recovery residences provide essential support for individuals recovering from SUD, yet their presence in residential neighborhoods often raises concerns on potential decline in property values. Our study examines the relationship between presence of recovery residences and median home values at the census tract level in Texas.


METHODS
We used publicly available tract-level data on area, total population, housing units, CDC/ATSDR Social Vulnerability Index (SVI), and median home values. Data on the recovery residences were obtained from a cross-sectional statewide survey of substance use service providers. Census tracts with and without recovery residences were matched using a k-nearest neighbors (KNN) algorithm. Logistic regression and permutation tests were used to assess differences in median home values between matched tracts.


RESULTS
Results from comparison of 329 census tracts with recovery residences and 1161 matched controls showed no significant association between presence of recovery house and property values, after adjusting for p",1.0,
dd435164f5c3ce7577ddeade4763af693a7e18e3,10.54254/2754-1169/2024.ld19019,Exploring the Impact of China's Economic Dynamics on the Real Estate Market: 2002-2022,2025.0,"Advances in Economics, Management and Political Sciences",Xinzhao Wang,"The complex interaction between China's economic dynamics from 2002 to 2022 and its real estate market is investigated in this article. It looks at the market's influence as a main economic driver, its relationships with the financial system, and regional differences in investment impacts. By means of regression analysis, the study indicates a noteworthy positive association between housing prices and the Consumer Price Index (CPI), therefore implying that inflationary pressures raise real estate values. The results underline the importance of cautious policy management to reconcile economic development with housing affordability, hence addressing regional differences and the wider consequences of inflation on housing markets. Also, the paper investigates how income inequality, urbanization rates, and government policies could shape the expansion of the real estate market. It emphasizes the importance of region-specific policies to strike a compromise between social equality and economic growth. The results give legislators trying to steady the housing market among changing economic circumstances important new perspectives.",1.0,https://www.ewadirect.com/proceedings/aemps/article/view/19019/pdf
f6054682589d21e7a4cb5ecb744ac85384b37b16,10.48550/arxiv.2508.02765,The Architecture of Trust: A Framework for AI-Augmented Real Estate Valuation in the Era of Structured Data,2025.0,arXiv.org,Petteri Teikari; Mike Jarrell; Maryam Azh; Harri Pesola,"The Uniform Appraisal Dataset (UAD) 3.6's mandatory 2026 implementation transforms residential property valuation from narrative reporting to structured, machine-readable formats. This paper provides the first comprehensive analysis of this regulatory shift alongside concurrent AI advances in computer vision, natural language processing, and autonomous systems. We develop a three-layer framework for AI-augmented valuation addressing technical implementation and institutional trust requirements. Our analysis reveals how regulatory standardization converging with AI capabilities enables fundamental market restructuring with profound implications for professional practice, efficiency, and systemic risk. We make four key contributions: (1) documenting institutional failures including inter-appraiser variability and systematic biases undermining valuation reliability; (2) developing an architectural framework spanning physical data acquisition, semantic understanding, and cognitive reasoning that integrates emerging technologies while maintaining professional oversight; (3) addressing trust requirements for high-stakes financial applications including regulatory compliance, algorithmic ",1.0,
de0523702e2eebe77da4b6ff416c73a2f2ba257e,10.54097/tgr4zy70,Exploring the Determinants of Housing Prices: Insights from the U.S. Market,2025.0,"Highlights in Business, Economics and Management",Si-Tong Wang,"Housing prices significantly influence economic stability, household wealth, and urban development, making their determinants a crucial area of study. This study investigates the determinants of housing prices in the U.S. market, focusing on structural, locational, and temporal factors. Using a dataset of 4,600 housing samples from Kaggle, the research employs multiple regression analysis and interaction models to quantify the effects of variables such as living area, year built, and waterfront presence. Results show that additional living space, bathrooms, and waterfront houses have positive price effects, while construction year and additional bedrooms have adverse effects. Interaction effects also show that older houses have diminishing price effects from the living space. However, serious multicollinearity problems with the interaction model raise concerns about exercising caution when interpreting and using alternative methods. There are implications for policymakers looking for greater affordability, estate agents setting price policies, and researchers using new modelling techniques to research housing markets.",1.0,https://hbem.org/index.php/OJS/article/download/642/592
8b3f3a2bc764ad2eb5fc96d5acd25ed14bf4ee97,10.36948/ijfmr.2023.v05i04.5255,Prediction and Analysis of House Price Through Machine Learning Approach,2023.0,International Journal For Multidisciplinary Research,Manivel Kandasamy; Dr. Raju Shanmugam; Ayush Dave; Charvin Chawda; Krish Shah; Umang Seladiya,"In recent years, picture detection, spam restructuring, natural speech command, product recommendation, and medical diagnosis have all benefited greatly from machine learning. The current machine learning algorithm aids in improving medical advancements, public safety, and security alerts. In the current work, we talk about the machine learning algorithm-generated forecast of future home prices. We compare and investigate numerous prediction methods for the selection of prediction methods. We employ lasso regression as our model because to its flexible and probabilistic model selection process We construct a housing cost prediction model in the considering such machine learning algorithm models as XG Boost, lasso regression, and neural systems on look at their order precision execution. At that point, we urge a real estate agent or home seller to employ a housing cost estimates model to assist them with better information based on property valuation. In regards to precision those tests show that the lasso regression algorithm constantly beats alternative models when utilized to predict costs associated with housing.",1.0,https://www.ijfmr.com/papers/2023/4/5255.pdf
beac88618c5e28b95084ffc99e1d3a9fc6ad8353,10.21861/hgg.2013.75.02.02,"Spatial Analysis of the Sustainability of Small Communities: A Case Study on the Island of Ilovik, Croatia",2014.0,,E. Rogen; M. Zlatić,"This paper outlines the primary research conducted on the small island of Ilovik, Croatia and the following spatial statistical analysis performed with a central goal of examining current sustainability of the village and providing future planning recommendations. For two weeks in mid-June of 2011, residents were interviewed about their current socioeconomic status and perceptions of tourism, the natural environment, and government. Their responses were assigned numeric values with dummy variables excluding the least common response, and were entered as attributes of building footprint polygons digitized in a GIS to allow for spatial statistical analysis. Tests included Local and Global Getis Ord G, Ordinary Least Squares, and Geographically Weighted Regression. Clear hot and cold spots were found, showing where additional water sources are needed or where water quality should be addressed and that a coastal vs. inland divide exists. Weak correlations existed between income and all other attributes, but from the results one could suggest a focus on increasing tourism and decreasing the supply of houses for sale.",1.0,https://hrcak.srce.hr/file/167808
579b29185c6a16af482e706d2a82a2f2d79819e5,10.1109/iciip61524.2023.10537786,Enhancing House Price Prediction Accuracy using Deep Learning and Hyperparameter Tuning with AEDA,2023.0,International Conference on Intelligent Information Processing,Ramesh Priya; K. Raghuveer; K. Srinivas; B. A; P. J. Shriidhar; N. Shirisha,"Traditionally, real estate agents and appraisers used their expertise and market knowledge to estimate house prices. However, due to the expansion of data sources and developments in data science, there was a growing interest in developing predictive models to automate and enhance the house price prediction process. This research introduces the Adaptive Exploratory Data Analysis (AEDA) tool, specifically the AEDA by frequency for categorical values, implemented in Python. The tool provides an API that maps usual AEDA tasks in statistical modeling for generating plots and statistics. It automatically generates insights and establishes standards and procedures. The frequency of users in various variables highlights the challenges in data processing pipelines for predicting house prices using Machine Learning (ML) and Deep Learning (DL) techniques. The chapter also discusses the evaluation of the hyperparameter tuning model for house pricing in the subsequent chapter. Model building, in the context of ML, involves creating a model that can learn from data without explicit instructions. Regression analysis is utilized to observe and resolve prediction issues. The suggested house price ",1.0,
b2a27edffca125304777469c2ab1e77ece301558,10.54254/2754-1169/120/20242519,Macro Indicators and Housing Prices in the U.S.,2024.0,"Advances in Economics, Management and Political Sciences",Yu-Ming Zhang,"This study examines the influence of macroeconomic indicatorsU.S. GDP, tax rates, population, and interest rateson housing prices across the United States, utilizing data from 1982 to 2018 sourced from the Federal Reserve Economic Data (FRED). Employing a comprehensive analytical framework, including multiple linear regression, time series analysis, and a mixed-effects model, the research identifies GDP and tax rates as significant factors affecting housing prices. GDP positively correlates with housing prices, whereas higher tax rates have a negative impact. The study highlights the importance of accounting for regional variations through mixed-effects modeling, which captures the diverse impacts of economic indicators across different states. This approach offers nuanced understandings to the real estate market's dynamics, emphasizing the role of economic growth and fiscal policies. The findings aid policymakers in understanding the economic forces shaping housing markets and suggest further research avenues, particularly in incorporating recent global economic events like the COVID-19 pandemic.",1.0,https://aemps.ewapub.com/article/view/16279.pdf
55c940d832ef8dcdad2835c8fc50397dc9fa7c33,10.30595/juita.v14i1.28582,Enhancing Geospatial House Price Prediction in Greater Jakarta Using XGBoost and ResNet18 Feature Fusion,2026.0,Jurnal Informatika,Hadi Santoso; Alif Biuti Anastasya,"Precise house price predictions play a vital role in shaping housing policies and informing investment decisions in urban regions such as Greater Jakarta, encompassing Jakarta, Bogor, Depok, Tangerang, and Bekasi. Most models rely exclusively on structured data, ignoring spatial and environmental factors that influence property prices. This study proposes a multimodal machine learning framework that integrates structured property data with Sentinel-2 satellite imagery within a geospatial context. The baseline dataset consists of 17 tabular variables. The ResNet-18 algorithm extracts visual environmental information from satellite imagery. The integration of both modalities through a late fusion strategy results in improved predictive performance. The baseline XGBoost achieved R² scores of 0.81 (log scale) and 0.79 (Rupiah), with an error of about 184 million Rupiah. The image-only model achieved an R² of 0.43, indicating moderate explanatory capability. Late fusion further improved performance, achieving R² values of 0.94 (log scale) and 0.93 (Rupiah), while reducing prediction error by over 40%.",1.0,https://doi.org/10.30595/juita.v14i1.28582
76f161e1c15ab1d813cee869a310096fbaf4725f,10.2139/ssrn.5160602,Impact of Institutional Owners on Housing Markets,2025.0,Social Science Research Network,Caitlin Gorback; Franklin Qian; Zi-Pei Zhu,"Since the Great Recession, the rise of single-family rental companies has changed the investor ownership landscape in the U.S. Using housing transaction data, we document the rise of Long Term Rental (LTR) companies by constructing a panel of national single-family housing portfolios. We show that LTR growth outstripped all other investor types and that these companies geographically concentrate their holdings, expanding their local market shares over time. We construct a novel instrument predicting LTR entry, leveraging differential revealed preferences in product characteristics across landlord types, interacting with a proxy for falling property management costs over time. We use this instrument for LTR market entry to estimate the causal impact of LTR market share on local house prices. We find that a 1-standard deviation above the mean increase in LTR share growth leads to an annual additional house price growth of 2.11pp. Finally, we discuss how the reallocation of homeownership across small and large landlords, as well as owner-occupants and investors, contribute to these price increases.",1.0,https://doi.org/10.2139/ssrn.5160602
9956aba8c110f602a88fd2b3743617f75e16f997,10.54254/2755-2721/45/20241031,Research on predicting Ames housing price based on forward selection regression and principal component regression,2024.0,Applied and Computational Engineering,Ziwen Wang,"This study aimed to predict home prices in Ames, Iowa, using two regression techniques: forward selection regression and principal component regression. The research began with exploratory data analysis and several pre-processing steps. Features are classified, overlapping features are merged, missing values are processed appropriately, and predictors of zero or near-zero variance are removed. The data is then scaled to solve the problem of data fragmentation. The results indicated that the forward selection regression method had higher prediction accuracy and a lower root mean square error (RMSE) value, suggesting its superiority in predicting home prices. The findings of this study have practical implications for various stakeholders in the real estate market. By focusing on the identified areas that contribute significantly to the value of real estate, stakeholders can make informed decisions to enhance their investments. This research provides valuable guidance for homeowners, real estate agents, and property developers in understanding the factors influencing home prices in Ames, Iowa.",1.0,https://ace.ewapub.com/article/view/10752.pdf
31d195fbe803bb066a9fb0cae093a628f2a6bafd,10.54254/2753-8818/39/20240571,The research on factors influencing house value-take California as an example,2024.0,Theoretical and Natural Science,Tian-Zheng Li; Xuezhou Yang,"Housing price is a popular and important topic in todays society. This article aims to find the factors that have impacts on the housing price. To find the relationships between factors, this article uses Multiple Linear Regression as the method to perform a significant analysis of factors. 1000 samples of Californias block groups in 1990 are selected for this research. Based on the assumption, this research chooses 8 explanatory variables for the analysis. Because of the relationships between explanatory variables, the article also adds interaction terms between latitude and longitude, and population and total bedrooms to solve the multicollinearity problem among explanatory variables. To optimize model analysis effectiveness, this research compares the significance, VIF value, and GVIF value of explanatory variables. The analysis result shows that the geographical location (Latitude and longitude), the housing median age, the total bedrooms, the population, and the median income make significant impacts on the housing value. Among these factors, the median income is the main factor.",1.0,https://www.ewadirect.com/proceedings/tns/article/view/13306/pdf
b8b23f0dcf1a4b3bb6fccf6cd81535d931b9ca3d,10.24957/hsr.2019.27.2.117,A Study on the Influence Factors on Residential Mobility Plan to Non-Capital Area of Households in Capital Area,2019.0,Korean Association for Housing Policy Studies,Mihwa Lim,"The residential mobility of households to the metropolitan area has led to the rapid rise in house prices, traffic congestion, and environmental problems in urbanized locations. On the other hand, in the case of non-metropolitan areas, there are many problems due to population decline and a shrinking urban scale. To solve such urban problems, it is necessary to explore residential mobility. This study empirically analyzed the factors affecting the residential mobility plan of households in the capital area, and migration to the non-capital area through the five-year Korea Housing Survey. According to the results of this study, the characteristics of households that are planning to move to non-capital areas are as follows: Rental households in the capital area, households without school-aged children, households with a low living cost compared to living expenses, and households that are planning to own a house. Considering that these characteristics were significant, while the age of the household resident and other household characteristics were not significant, it can be implied that, in the case of the non-capital area, residential mobility is not due to the voluntary choice of h",1.0,
39f20b64c3740a48ef1cc318f2e8fe9304b0a720,10.21098/bemp.v23i4.1043,INTERNAL AND EXTERNAL DETERMINANTS OF HOUSING PRICE BOOM IN HONG KONG,2020.0,Buletin Ekonomi Moneter dan Perbankan,Farhad Taghizadeh‐Hesary; Naoyuki Yoshino; Aline Mortha; Alvin Chiu; Niki Naderi,"Hong Kong’s housing market witnessed a dramatic housing price appreciation in recent years, with the price index for private domestic housing units being three times higher than ten years ago. This trend is supported by both internal and external factors, as illustrated in this paper. By developing a theoretical model and an empirical analysis on the key variables influencing housing prices using monthly data from 1999 to 2018, we find that the main drivers of housing price appreciation are from the demand side and include income level, money supply and inflation. The main contribution of this study is the quantification of the role of Mainland China’s macroeconomic factors in housing price booms in Hong Kong. Our study shows that capital inflow from and inflation and recessions in Mainland China contribute to increasing housing prices in Hong Kong because the city’s real estate is seen as a way to preserve asset value. These findings call for the need for control of capital inflow between the two economies as well as for stricter regulations against empty houses in Hong Kong.",1.0,https://www.bmeb-bi.org/index.php/BEMP/article/download/1043/943
872e9688c02319984f92a71aaeb850d6fee0ec70,10.1111/cag.70032,An over time comparison of linear and spatial regression model parameters to predict average housing values: Evidence from two Canadian cities,2025.0,Canadian Geographies / Géographies canadiennes,Hanna Maoh; M. Abdo,"Residential real estate models are a key component of planning support systems, which aid in shaping future urban land use and transportation. These models typically rely on parameters estimated from historical data corresponding to a specific base year, assuming that such parameters remain stable over time. This paper examines the temporal stability of parameters linked to variables influencing average housing values at the dissemination‐area level. It does so by comparing parameter estimates across multiple time periods in two Canadian cities: Ottawa, Ontario and Calgary, Alberta. Using Canadian census data, pooled regression analyses were conducted for the years 2006, 2011, and 2016 to model average housing values. The findings indicate general consistency in the factors affecting housing values across both cities. Interestingly, the parameters of the Ottawa pooled models suggest strong stability over time compared to Calgary. Additionally, the Spatial Autoregressive model outperformed both linear and spatial error models in terms of accuracy and predictive performance.",1.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/cag.70032
991834b04e43800cbdf1e4b9c99d804f3729f1ca,10.1109/geoinformatics57846.2022.9963833,Taxi travel time based Geographically Weighted Regression Model (GWR) for modeling public housing prices in Singapore,2022.0,GEOINFORMATICS,Yi'an Wang; Fangyi Cai; Shih-Fen Cheng; Bo Wu; Kai Cao,"In this research, a taxi travel time based Geographically Weighted Regression model (GWR) is proposed and utilized to model the public housing price in the case study of Singapore. In addition, a comparison between the proposed taxi data driven GWR and other models, such as ordinary least squares model (OLS), GWR model based on Euclidean distance and GWR model based on public transport travel time, have also been carried out. Results indicates that taxi travel time based GWR model has better fitting performance than the OLS model, and slightly better than the Euclidean distance-based GWR model, however, it is not as good as the GWR model based on public transport travel time according to the metric of Adjusted R2. These experiments indicate that the public transport travel time may has a major part to play in modeling the public housing resale prices compared to taxi travel time or driving time, and both the taxi travel time and public transport travel time can better explain the public housing resale prices in Singapore compared to Euclidean distance in the GWR modeling.",1.0,https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=8711&context=sis_research
a461004b46cd73f66a89290e5cfd637527079bca,10.62051/erpm8q62,Analysis of machine learning methods applied to financial problems,2024.0,Transactions on Computer Science and Intelligent Systems Research,Jia-Yi Zhang,"With machine learning being widely used in many fields. In this paper, we study how machine learning methods can be used to make predictions in financial problems and analyze the applications.Machine learning is a branch of artificial intelligence that enables computer systems to learn from data and make decisions or predictions without being explicitly programmed at every step. Machine learning models are trained on large amounts of data to discover patterns and relationships in the data and use these patterns to make predictions or perform tasks. The purpose of this post is to illustrate the role machine learning plays in dissertation research.Machine learning can also be used for smart urbanization, industrial automation, fintech, healthcare, agricultural modernization, cybersecurity, etc. For example, extracting valuable information and knowledge from large amounts of data to help businesses and organizations make better decisions. By analyzing historical data, machine learning can predict future trends and behaviors, such as stock prices, house prices, weather, etc.",1.0,https://wepub.org/index.php/TCSISR/article/download/3418/3761
95805c397eb60cbc179b0ec443b4d81119139b97,10.1051/matecconf/201925702001,Grid Convergence Analysis for MUSCL-based Numerical Scheme in Shockwave-containing Flows,2019.0,MATEC Web of Conferences,Guangning Li; D. Bhatia; Min Xu; Jian Wang,"This paper investigated the influence of limiter functions widely utilized in MUSCL-type (Monotone Upstream-centred Schemes for Conservation Laws) upwind numerical schemes on the solution accuracy of shockwave-containing flows. An incident shock interacting with laminar boundary layer developed on a flat plate was numerically simulated with the in-house developed code. A mixed-order grid convergence study was performed to assess the spatial errors of different limiters in simulating the selected shockwave-containing flow on flat plate. The conclusions are that, limiter functions implemented in the current in-house code play the critical roles in accurately predicting shockwave-containing flows. The mixed-order error estimator based on grid convergence study was proved to be applicable to evaluate the spatial errors of shockwave-containing flows, where the shock could reduce the nominal second- or third-order accuracy to first-order. The mixed-order estimator is conservative in the sense that the actual error is less than the error estimated, in the examined case.",1.0,https://www.matec-conferences.org/articles/matecconf/pdf/2019/06/matecconf_pmae2018_02001.pdf
f59be55ef71dbbb241f895d939bfe981c245fd84,10.54097/hset.v39i.6638,Comparative Analysis of Machine Learning Performance in House Price Prediction for Ames Iowa,2023.0,Highlights in Science Engineering and Technology,Xingchen Zhou,"The price of housing is closely related to the quality of people's lives, which is reflected in various aspects (e.g., finding a place to live and managing their finances). On this basis, being able to predict house prices would be a great convenience, and this study examines the accuracy of multiple mechanistic learning regression models in predicting future house price changes. To be specific, this paper downloaded the dataset from Kaggle which includes the training and testing csv files. and ran many different regression techniques including lasso and more models on google Colab. Furthermore, a comparison between the results of different methods is carried out by estimating the mean square error. Then, this research combined some of the best yielded results of the model to combine into a final model and got a result with a high score. According to the analysis and evaluations, the study concluded that the hybrid model resulted in a more accurate prediction than the single model. These results shed light on guiding further exploration of real estate prediction.",1.0,https://drpress.org/ojs/index.php/HSET/article/download/6638/6432
c18751edf30a3d6a24bad808d08747e396f9d7f1,10.1109/fists46898.2020.9264857,Influence model of regional taxi travel demand based on Geographical weighted regression,2020.0,2020 Forum on Integrated and Sustainable Transportation Systems (FISTS),Jian-Cheng Weng; Hanmei He; Yuan Wang; Ke Zhang,"Accurately mining the spatiotemporal distribution characteristics of demand for taxi travel is helpful to better dispatch and guide the distribution of taxi supply, so as to alleviate the imbalance of taxi supply and demand in high passenger demand areas. Based on the multi-source traffic data including taxi GPS data, taximeter data, public transport transactions data and Point of Interesting (POI) data in Beijing, correlation analysis methods were used to select the influencing factors of taxi travel demand, and establish a multi-dimensional set of influencing factors. A Geographical Weighted Regression (GWR) based influence model of regional taxi travel demand is established, and 1398 regions in Beijing is taken as an example to quantitatively explore the impact of various factors on taxi demand under different space-time conditions. The results show that the density of high-grade residential area, financial and commercial land, office area and recreational area in the city periphery has an obvious positive impact on taxi travel demand; While the density of residential land in urban periphery and the office area in the city center has a negative correlation with the taxi travel d",1.0,
59deef5440599497c8f622abbe85f977745df929,10.59934/jaiea.v4i3.1047,House Price Prediction Analysis Using Linear Regression and Random Forest Algorithms,2025.0,Journal of Artificial Intelligence and Engineering Applications (JAIEA),Hanspran Limbong; Mufti Alwisyah Lubis; Mhd. Furqan,"This study aims to analyze house price prediction using two machine learning algorithms: Linear Regression and Random Forest. Quantitative evaluation is conducted using four main metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R² Score, and Mean Absolute Percentage Error (MAPE). The experimental results show that the Random Forest model outperforms Linear Regression in all four evaluation metrics. The MAE and RMSE of the Random Forest model are lower, indicating that this model is more effective in minimizing prediction errors. Additionally, the higher R² Score demonstrates the model's better ability to explain house price variance, while the smaller MAPE indicates more accurate prediction errors in the context of real estate. These findings suggest that choosing the right algorithm is crucial for modeling complex house price data, and although Random Forest is more accurate, its black-box nature limits interpretability. Therefore, for future research, more interpretable methods such as XGBoost with SHAP analysis could be considered.",1.0,https://ioinformatic.org/index.php/JAIEA/article/download/1047/758
6b33b5003b996cd04e647ce3a57c9b0aa30781c0,10.54254/2755-2721/32/20230222,"House price prediction using machine learning: A case study in Seattle, U.S.",2024.0,Applied and Computational Engineering,Jiapei Liao,"In recent years, predicting housing prices has become a prominent research topic. The motivation behind this research is the lack of precise analysis and comprehensive comparison of different machine learning models for Seattle housing prices. To address this issue, this paper utilizes a dataset obtained from Kaggle, consisting of Seattle housing prices. This study focuses on analyzing and comparing the performance of different machine learning models in predicting housing prices in Seattle. After preprocessing the data, eight different machine learning models are applied to predict housing prices in Seattle. A comprehensive comparison of these models is conducted to analyze their differences. The experimental results show that the differences in performance among the models are not substantial. However, StackingAveragedModels emerges as the top-performing model, with an RMSLE of 0.2328 and an R2 of 0.7771. These findings contribute to a better understanding of the predictive capabilities of different machine learning models for Seattle housing prices.",1.0,https://www.ewadirect.com/proceedings/ace/article/view/9948/pdf
4eea76f80dfaacd7412ecc66186284658da6c224,10.1007/s11187-025-01141-z,The rise of nonemployer businesses in the United States: an analysis by geography,2026.0,Small Business Economics,Tessa Conroy; Austin Landini; M. McDermott,"
 During the first two decades of the 2000s, the number of nonemployer businesses grew remarkably, far outpacing the growth in the number of employer businesses. We analyze the factors that may be driving the growth of nonemployer businesses per capita at the county level. We pay particular attention to geography, namely the rural and urban contexts and potential spatial spillovers between counties. We find that nonemployer growth is highest in places with large non-White populations as well as low incomes and home values, suggesting that nonemployer business ownership may be a form of necessity entrepreneurship that is more common in low-wealth areas. Yet, nonemployer growth is also higher in places with more small business lending, indicating the important role of capital even for very small businesses. Using a panel Spatial Durbin Model and Geographically Weighted Regression (GWR) to assess the importance of spatial spillovers, we find that the main results are largely robust to spatial models, but the GWR model highlights important local insights.",1.0,https://link.springer.com/content/pdf/10.1007/s11187-025-01141-z.pdf
f3686557fef80eaec287abc31c8f6144a37fa150,10.32910/ep.70.6.1,House prices in the euro area,2019.0,Ekonomski Pregled,Silvo Dajčman,"The paper aims to
assess whether financial market stress is associated with real house prices in
the euro area. Building on the theory of house prices fundamentals, we first
apply the second generation cointegration tests and reject a stable long-run relationship between house prices and
the variables identified in the theory as their main determinants
(fundamentals). Short-run panel data models are
then estimated, relating real house prices to their fundamentals and the
financial market stress. The results imply that the growths in the real GPD per
capita and the loans to households for house purchase are the main determinants
of real house prices growth in the short run. Financial market stress is
significantly associated with real house prices changes only in some euro area
countries. Different panel data estimators are used to show that heterogeneity
and cross-section dependence needs to be accounted for to obtain robust estimates. The differences between two groups of euro area
countries (the PIIGS and the non-PIIGS euro area) are also compared.",1.0,https://hrcak.srce.hr/file/338582
76bfb38e634b9191cdbf366d57ede28296fd9cf7,10.1080/19407963.2025.2499496,"Second homes in Spanish municipalities: regulatory, economic, and geographic determinants",2025.0,"Journal of Policy Research in Tourism, Leisure and Events",Susana Herrero Ballesta,"ABSTRACT In recent decades, second homes have gained popularity for recreational use and as investment properties through holiday rentals. Platforms like Airbnb have facilitated this trend, generating income and boosting economic development, particularly in tourist and rural areas. However, this growth has led to challenges, including rising property prices, strain on local infrastructure, and reduced housing affordability. In Spain, second homes are mainly found in coastal regions, rural areas, and tourist spots near major cities. Previous research has primarily focussed on regional or provincial levels. This study investigates the factors influencing the second homes supply across Spanish municipalities. Utilizing data from 151 municipalities for the year 2021, sourced from the Instituto Nacional de Estadística (INE), and incorporating variables such as short-term rental regulations, socioeconomic characteristics, location attributes, and leisure factors, we apply an econometric model with quadratic terms. This approach allows us to capture potential non-linear relationships. The findings show that short-term rental regulations, such as limits on rental days and restrictions in ",1.0,
95df7befc028f3dece860acaa22d70e24895e508,10.1145/3650400.3650551,Machine Learning-Based Study on Airbnb Housing Price Prediction——Evidence from Hong Kong,2023.0,EITCE,Qing-Xiao Wang; Li-Hua Huang,"The emergence of the sharing economy has brought about transformative changes across various industries. A prominent example, the Airbnb platform, has introduced a new business model to the hospitality sector, enabling numerous microenterprises to thrive through unique pricing strategies. A comprehensive understanding of these pricing strategies is crucial for exploring the essence of this innovative business model. This paper analyzes a dataset of 6,481 property listings from Airbnb.com in the Hong Kong region as of June 24, 2023. Leveraging natural language processing and machine learning techniques, a price prediction model is developed. The study employs three distinct prediction methods: Linear Regression, Random Forest, and XGBoost. By comparing their predictive performances, the Random Forest method demonstrates the strongest predictive capability. Furthermore, an attribution analysis is conducted using the Random Forest model, aiding hosts in setting appropriate prices and creating a stable and equitable rental environment for tenants.",1.0,https://dl.acm.org/doi/pdf/10.1145/3650400.3650551
31762742cc42aa49ed71699e44ecc8655603ee42,10.24957/hsr.2025.33.2.5,Examining Sub-Tenure Choice of Renter Households Amid the Expansion of Rental Deposit Loans: Focus on Changing Roles of Economic Characteristics and Publicly Supported Deposit Loans,2025.0,Korean Association for Housing Policy Studies,Yongchan Jung; Hyunho Ahn; Seungbeom Kang,"Since the 2000s, the rental market in South Korea has undergone a structural shift, characterized by a decline in Jeonse (deposit-only renting) households and a rise in Wolse (monthly-paying renting) households. In this context, government-backed loans for rent deposits have significantly expanded as a policy tool to reduce housing cost burdens. However, previous studies have focused on socioeconomic determinants of tenure choice, with limited attention paid to the role of deposit loans in shaping sub-tenure choice. To address this gap, this study examines whether income, assets, and policy-supported loans for deposit significantly influence the sub-tenure choice between Wolse and Jeonse, and if so, how the magnitude of the influence changed over time. Using data from the Korea Housing Survey, the analysis reveals that households with higher incomes, lower assets, and without access to policy-supported loans are more likely to choose Wolse over Jeonse and to reduce the proportion of rent paid as a deposit. Notably, the effect of utilizing deposit loans has increased over time, weakening the association between household income and both tenure choice and deposit proportions. These f",1.0,
5a954502f7eb3f82037afa45e4edbfa98e3b0207,10.5937/bankarstvo2401038a,Determinants of the real estate market in the Republic of Serbia: Overview and findings,2024.0,Bankarstvo,Almir Alihodžić,"Value changes in the real estate market affect both the quality of bank loan portfolios and financial stability and the real economy. The Republic of Serbia is one of the countries facing an upward trend in demand for housing. This paper investigates the impact of macroeconomic and banking variables on the real estate price index in Serbia for the period from 2014 to 2023 on a quarterly basis. Also, panel regression and correlation analysis are applied in this research. The selected independent (explanatory) variables are the gross domestic product, the consumer price index, the interest rate on bank loans, the exchange rate of the domestic currency against the Euro and household saving. The research results showed that the independent variable consumer price index had the most significant impact on the housing price index. On the other hand, the following independent variables had the most significant negative impact on the dependent variable (housing price index): interest rate on bank loans and the domestic currency against the Euro.",1.0,http://dx.doi.org/10.5937/bankarstvo2401038a
73fca727a82541fadd8fc432dbc43b0a13b655b4,10.1016/j.sbspro.2016.05.280,Forms and Norms: How Planning Affected Housing Prices Variation in Italy During the Crisis,2016.0,,V. Antoniucci; G. Marella; Stefano Moroni,"Abstract According to numerous studies in the literature, urban planning norms have an important effect on housing prices. A large quantity of data and empirical studies focusing on building restrictions and housing prices refer to the American real estate market. The current study, instead, set out to examine the relationship between city-planning and the cost of housing in the Italian real estate market. While, however, recent studies carried out here have prevalently focused on the boom phase of the real estate cycle, the present study primarily probes the bust period. We used a multivariate regression model to analyze the effect of urban planning norms on housing (new and existing units) price variation. The model shows that in those localities where building regulations had permitted more buildings to be constructed during the boom phase, prices fell to deeper level during the bust one. These findings have implications with regard to the real estate crisis as well as to housing affordability policies here in Italy and elsewhere.",1.0,https://doi.org/10.1016/j.sbspro.2016.05.280
765e06c5e5f4ddc7dbbc22ffe233712dca5dd0a2,10.1038/s41372-026-02728-4,"Gentrification, measures of neighborhood change, and infant mortality in Michigan",2026.0,Journal of Perinatology,D. Murosko; M. Passarella; D. Montoya-Williams; Roshanak Mehdipanah; S. Lorch,"Determine the association of infant mortality (IM) and gentrification, a composite measure of rising neighborhood income, education, and housing costs. Retrospective cohort analysis of 672,432 infants born 2010–2019 across metropolitan Michigan. Multilevel multivariable regression models examined the association of gentrification and IM. Gentrification components were evaluated in separate models. Gentrification was not associated with IM, but some of its components were. Living in a census tract with an above median rise in income or education attainment was associated with lower odds of IM, compared to below median change (Income: aOR=0.88; 95% CI: 0.82–0.95; Education: aOR=0.93; 95% CI: 0.86–0.997). Rent increases above the median were linked to higher IM among term infants and lower IM among infants <32 weeks’ gestation. The association of IM with rising home values varied by maternal insurance status. Indicators of increasing community affluence, often combined to define gentrification, have opposing relationships with IM.",1.0,https://doi.org/10.1038/s41372-026-02728-4
84258eb0b1c420878660b749c8faa7b9c82ad8f6,10.1177/09697764231186746,Losing a job and (dis)incentives to move: Interregional migration in Finland,2023.0,European Urban and Regional Studies,Terhi Maczulskij; P. Böckerman,"Traditional theories of migration frequently fail to fully explain the real-world patterns of interregional mobility. Empirical studies indicate that individuals may not always migrate for economic reasons such as poor employment and income prospects. Which characteristics drive people to stay or move after job displacement? Using information on establishment closures, we analyse the economic and social determinants of interregional mobility following job loss. We base our empirical analysis on nationwide individual-level register data from Finland for 1997–2015. We find that receiving earnings-based unemployment benefits substantially weakens the economic incentives for interregional mobility. This negative association is particularly strong for the lower educated and those living in more rural areas. Moreover, our results show that the migration decisions of displaced workers are strongly affected by homeownership, differences in regional housing prices, and social connections, as measured by childhood family relations.",1.0,https://jyx.jyu.fi/bitstreams/74ab228b-2544-409a-b773-80b32637d67d/download
333ddb60656ff6f0c1a37bf648c52963531a765b,10.5392/jkca.2016.16.07.207,A Study on The Characteristics of Residential Area of Housing Voucher Program - in the Case of the Seoul Metropolitan Area,2016.0,,Ga-yeon Kim; Heejeong Hong; Sunghyun Hong,"Paradigm for supporting housing for low-middle income class has been changing from public rental housing to Housing Voucher. Housing Voucher started first in Seoul since 2010, and it has been expended to other areas in 2014. Given the dearth of previous research data, this study aims to analyze options determinants that the beneficiaries could consider in choosing their residential area. In this study, the researcher used for the research methods, a quantitative analysis by Geographically Weighted Regression (GWR) and Ordinary Least Square (OLS) has been conducted. As a result, the accessibility to social welfare centers, public transportation and job opportunities emerged main factors to for the Housing Voucher recipients in Seoul to choose their residential area. This is different results from previous research, which has two implications. First, reexamination of Housing Voucher is necessary. Second, Housing Voucher beneficiaries should include not only the housing but also support for family and welfare system access.",1.0,http://society.kisti.re.kr/sv/SV_svpsbs03V.do?method=download&cn1=JAKO201624557928685
432fe7d36188d6b08a832caf4c2458d2f2896e2a,10.37407/kres.2024.42.2.135,Real Estate Jeonse Using Text Mining Meta-Analysis of Research Trends: Focusing on Articles Published in KCI-Registered Journals,2024.0,Korea Real Estate Society,S. Ha; S. Kim,"To identify trends and trends in real estate Jeonse research and suggest future research directions, this study conducted a meta-analysis of 477 real estate Jeonse-related articles published in KCI-recognized journals over the past 20 years from 2003 to 2023 through network analysis and topic modeling using keyword TF-IDF, a text mining technique. The main findings of the study are as follows First, in the meta-analysis of studies by time period, the keywords 'Jeonse', 'Jeonse price', and 'apartment' appeared in all periods, and the keyword 'long-term Jeonse housing' began to appear after 2009. Based on this, it was found that the main topics of interest to researchers expanded to studies centered on rental issues such as 'Jeonse rate' and 'offset', and recently reached major social issues of Jeonse housing such as 'Jeonse deposit', 'Jeonse fraud', and 'reverse Jeonse'. In addition, the topics classified through topic modeling were named as 10 in total, and the 10 topics were classified into 3 research topic groups:“Jeonse market and status in Seoul,” “Jeonse-related laws and systems,” and “Jeonse price and market analysis.” Based on the above analysis results, we can confirm that ",1.0,
eb83b823dc7d4cd79368947127d94433b85b297b,10.1111/stan.12336,Estimation and convergence rates in the distributional single index model,2023.0,Statistica neerlandica (Print),F. Balabdaoui; Alexander Henzi; Lukas Looser,"The distributional single index model is a semiparametric regression model in which the conditional distribution functions P(Y≤y|X=x)=F0(θ0(x),y)$$ P\left(Y\le y|X=x\right)={F}_0\left({\theta}_0(x),y\right) $$ of a real‐valued outcome variable Y$$ Y $$ depend on d$$ d $$ ‐dimensional covariates X$$ X $$ through a univariate, parametric index function θ0(x)$$ {\theta}_0(x) $$ , and increase stochastically as θ0(x)$$ {\theta}_0(x) $$ increases. We propose least squares approaches for the joint estimation of θ0$$ {\theta}_0 $$ and F0$$ {F}_0 $$ in the important case where θ0(x)=α0⊤x$$ {\theta}_0(x)={\alpha}_0^{\top }x $$ and obtain convergence rates of n−1/3$$ {n}^{-1/3} $$ , thereby improving an existing result that gives a rate of n−1/6$$ {n}^{-1/6} $$ . A simulation study indicates that the convergence rate for the estimation of α0$$ {\alpha}_0 $$ might be faster. Furthermore, we illustrate our methods in an application on house price data that demonstrates the advantages of shape restrictions in single index models.",1.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/stan.12336
338dafefad9fa8d851728e3181a761435b4fedf8,10.53383/100394,Resilience Tested: Interplay between Mortgage Channels and Natural Disasters on Housing Price,2024.0,International Real Estate Review,Ariyanto adhi Nugroho; Yunastiti Purwaningsih; Lukmanul Hakim; Suryanto Suryanto,"The existing body of literature on the interaction between mortgage channels and natural disasters on housing prices is limited. As a vast archipelagic nation, Indonesia is highly susceptible to natural disasters. Our research utilizes quarterly data from Indonesian provinces between 2012 and 2019, and employs panel regression to evaluate the influence of mortgage channels and natural disasters on housing prices. The results indicate varying impacts on housing prices based on the type and intensity of natural disasters, alongside regional development, which remain robust under various tests. Despite potential disruptions, the overall availability and effects of housing loans on housing prices remain relatively stable, thus highlighting the significant role of mortgage channels in shaping housing prices under different circumstances. This understanding has the potential to guide stakeholders in enhancing disaster management strategies and overseeing housing prices, and takes into consideration the distinct vulnerabilities found in each region. Due to the unpredictable nature of natural calamities, it is crucial to establish plans for pre-disaster strategies. Using risk-disaster clus",1.0,
241304a775a062c63bb456442add0736606521fa,10.3390/su141811720,Housing and Setting Constraints: The Portuguese Evidence,2022.0,Sustainability,A. Santos; Hélio Castro,"In the last few decades, Portugal has witnessed an extraordinary quantitative and qualitative transformation in housing provision. The pace of housing construction was so extensive that the contemporary real estate market is currently characterized by an excessive supply, vis-à-vis the resident population. In this study, we discuss the impact of the financial process on the housing sector in comparison with tenancy. We consider transaction prices of the housing assignments, either through acquisition or through tenancy. The recent shock resulting from the pandemic situation did not slow down house prices but caused a slight drop in rents. The model used proposes to analyze the fluctuations in prices and rents in the face of external shocks. In the residential market, the estimation is complex due to the many heterogeneous attributes of residential assets. Non-fluctuating variables, such as size, location, and external demand for homes, explain a large part of the variation in price levels included in the model.",1.0,https://www.mdpi.com/2071-1050/14/18/11720/pdf?version=1663575711
ebc2a6ebe3af4daa29c29d4aaf4557cd2a2ab21c,10.1080/00779954.2023.2226671,The effect of school zone on housing prices: evidence from a quasi-natural experiment in New Zealand,2023.0,New Zealand Economic Papers,Peng-jie Sun; T. Coupé; J. Clark,"We analyse a quasi-experiment where a much sought-after state secondary school with no close substitutes unexpectedly reduced its enrolment zone twice over a three-year period. We use difference-in-differences to estimate the impact of the two downsizings on housing sales prices. We use controls for housing characteristics for pooled cross section or housing fixed effects for repeat sales, test for parallel trends, and conduct numerous robustness checks. In our main analysis, we find the first downsizing may decrease housing prices between 3.2% and 12.9%, with most estimates statistically significant, while the second downsizing may decrease prices between 2.1% and 7.2%, with most estimates insignificant. Tests in the pre-treatment period suggest parallel trends cannot be rejected, though some cross section interactions are significant when the two downsizings are analysed separately. We conclude the school zone’s first downsizing likely had a negative effect on housing prices of a small to moderate magnitude.",1.0,https://www.tandfonline.com/doi/pdf/10.1080/00779954.2023.2226671?needAccess=true
3aa7f030a4381b77e86adc6c4f71f107c8ae0bdf,10.2172/6301946,"Effects of nuclear power plants on community growth and residential property values. Final report, September 30, 1977--November 15, 1978",1978.0,,H. B. Gamble; R. Downing; O. H. Sauerlender,"This study tested the hypothesis that nuclear power plants adversely affect community growth and residential property values in nearby municipalities. Total assessed real property values from 1960 to 1976 for 64 municipalities and market sales data from 1975 to 1977 on 540 single family dwellings formed the data base, which was within 20 miles of 4 nuclear power plants in the Northeast: Pilgrim, Millstone, Oyster Creek, and R. E. Ginna. Analysis of the time series data showed that the averaged annual growth rates of total assessed values, in real terms, were inversely related to distances from the plants, and that growth rates for the years following plant construction were higher than for the years before plant construction, with the largest growth rate increase observed in the host municipalities. Multiple regression analysis of the cross sectional data explained about 80 percent of the variation in housing prices. The plants exerted no influence on the price of housing. The original hypothesis is rejected.",1.0,https://www.osti.gov/servlets/purl/6301946
2439f3f8e34b93bdf0936fce03d12a51bdbd5948,10.1109/acedpi58926.2023.00049,Research and Demonstration of a Prediction Algorithm Based on GBDT-LightGBM Algorithm,2023.0,"2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI)",Houzhi Chen; Zi-Chun Liu; Minyan Dai,"This paper aims to use the LightGBM algorithm to predict housing prices in Chinese municipalities. According to previous research experience, from the demand level, supply level, and regulation policy three main aspects as the main influencing factors of housing prices are analyzed and predicted. In the case analysis, the determination coefficient (R-Square) and the average absolute percentage error (MAPE) are used to test the accuracy of the model, and the Kendall tau-b (K) method in the Kendall coefficient is used for correlation analysis and consistency test. After eliminating the repeatability index, the Kendall coordination coefficient W of the model is 0.977. After selecting the appropriate influencing factors and data, the Light Gradient Boosting Machine is trained by using the gradient boosting decision tree GBDT as the base learner and SPSS-PRO software. It is found that the model has the highest accuracy when the number of base learners is 500. From the training results, the influence degree of demand and policy level is the largest, and the influence degree of supply level is small. The influence degree of the three is 47 %, 43 %, and 10 %. In the secondary indicators, t",1.0,
0563f027cf714e008ab3994408f3e369f4c1ed3b,10.56801/rebicte.v9i.156,Estimate House Price Using Machine Learning,2023.0,Research Briefs on Information & Communication Technology Evolution,Rishita Gadde; Md Amiruzzaman; Richard Burns; Md. Rajibul Islam; Rizal Mohd Nor,"The House Price Index (HPI) is commonly used to estimate the changes in housing prices. The sale price of the house is correlated with many other factors like geographical location, size of the house, age of the house, the area and population of the neighborhood etc. There has been a considerably large number of datasets released in the literature of various locations to explore the correlation of the sale price of houses with their corresponding features. However, all the features don’t affect the sale price in equal proportion. Some features strongly correlate with each other and, while some features don’t carry any importance or are probably redundant. As a result, to explore various impacts of features on sales prices, we performed a detailed data analysis on the original house dataset. This report also comprehensively validates multiple steps of data analysis with supporting statistics and visualizations to provide an optimistic result of various features and their impact on the sales price of houses.",1.0,https://rebicte.org/index.php/rebicte/article/download/156/195
77ca6815783b79bdf1bb76e3135c5029837c8fbe,10.1177/23998083251411954,A data-driven approach to spatial zoning and anomaly detection in the dynamic real estate network,2025.0,Environment and Planning B Urban Analytics and City Science,Satyaki Basu Sarbadhikary; Archi Roy; S. Deb,"We propose a novel statistical framework for dynamic zoning in real estate markets, leveraging spatial clustering and temporal change detection to ensure a robust and adaptive zoning mechanism. Our approach follows a two-stage procedure to systematically analyze zoning structures over time. First, we develop extended local indicators of spatial association algorithm to identify spatial clusters in a data-driven manner. These clusters are then integrated into a dynamic statistical network that incorporates geographical distance, capturing spatial dependencies and interactions between different regions. In the second stage of the algorithm, borrowing from the graph theory literature, we detect structural breaks in housing market dynamics using network Laplacians, which allow for identifying both gradual and abrupt shifts over time. This enables a precise understanding of how zoning structures evolve, distinguishing between short-term, long-term, and permanent shifts in spatial cohesion. Further, given the strong influence of zoning regulations on property pricing, we introduce a real-time property valuation score that captures market fluctuations while incorporating local spatial cha",1.0,
739575eb7e537e43a525d9a0f8a402b6b0ad981c,10.54254/2754-1169/2024.ld18572,Bifurcated Real Estate Market The Impacts of Home Office Trends on Commercial Real Estate and Urban Economics,2024.0,"Advances in Economics, Management and Political Sciences",Yinan Shen,"This paper explores how remote and hybrid work modes are changing the US real estate market in the wake of the pandemic. The research shows that there is a clear segmentation of the market. Specific industries such as technology, finance and professional services requiring high-end office space to support their operations and brand image. Retail, healthcare and manufacturing all have a high demand for physical space due to their specific industry characteristics. This creates regional differences, with high-demand areas able to thrive while others struggle. Futhermore, the paper suggests the implementation of rental subsidies targeted at physical sectors such as retail and dining. This is a benefitial way to increase the demand for real estate and reduce the tax burden on local governments. Additionally, it recommends the repurposing of unused real estate for public services which would drive economic growth. Overall this would increase economic stability and support a more balanced real estate market.",1.0,https://www.ewadirect.com/proceedings/aemps/article/view/18572/pdf
ce1c455ab32efd54f544601631d21401dc955aff,10.1080/01605682.2025.2513449,Enhancing house price forecasting with hybrid data driven feature extraction model,2025.0,Journal of the Operational Research Society,Kian Lun Soon; Lam Tatt Soon; Jia-Yi Teo,"Abstract Accurate house price estimation is crucial for stakeholders such as homeowners, investors, and policymakers. While current models excel at capturing spatial dependencies, they often overlook the influence of contextual factors such as macroeconomic conditions and property-specific attributes. To address this gap, the Variational Mode Decomposition and Random Forest (VMD-RF) feature extraction model is proposed to consider both spatial dependency factors (i.e. area, Gross Domestic Product (GDP), Consumer Price Index (CPI), and population) and contextual factors (i.e., tenure, age, and interest rate) in generating a unified housing market health index. Specifically, VMD decomposes each feature into intrinsic mode functions (IMFs), revealing underlying data patterns. The IMFs from all features are concatenated, and Random Forest performs dimensionality reduction, consolidating them into a single feature. This feature improves the model’s predictive power, as evidenced by a higher Spearman’s correlation coefficient (0.9652) with house prices. The contribution of VMD-RF lies in its enhanced prediction accuracy within a simpler machine learning framework, avoiding the complexity",1.0,
2a3ca691000fce1fa3c6fc5cd98dc6be6c8d00b1,10.16879/jkca.2020.20.2.071,Analysis of the Relationship between Internal Migration and the Housing Market in Small and Medium-Sized Cities: A Case Study of Gangneung,2020.0,Journal of the Korean Cartographic Association,Daeheon Cho,"The main purpose of this study is to analyze the patterns of internal migration in small and medium-sized cities in relation to the housing market. Examining the characteristics of migration by the county level administrative units shows that major regional cities have a relatively large proportion of internal migration that migrants do not cross the administrative boundary. In theses cities, not only the ratio of apartments to total housing is high, but housing related factors are the biggest reasons for migration. The spatial patterns of internal migration were analyzed with a focus on the housing market in case of Gangneung with high internal migration. The results are as follows. First, as intensifying internal migration with the large supply of new apartments since 2014, areas where the supply of apartments was concentrated showed high level of net in-migration, while existing areas with large populations showed high level of net out-migration. Second, analysis of OD flows revealed the dominant flows from most areas of Gangneung to areas with new apartments, and the sub-flows towards large residential areas where have suffered great net outflows. Third, there was much more mov",1.0,
bdd62d525d876160f3b7e2ed449cfdc9badd077c,10.1177/01600176241237180,Modeling Spatial Regimes With Smooth Transitions,2024.0,International regional science review,Ingrid Mattsson; Johan Lyhagen,"In this paper, we introduce a new way of modeling spatial regimes using smooth transitions. We propose an autoregressive spatial lag model where a logistic function captures structural variation in the spatial lag parameter. In the regime-switching spatial lag model with smooth transitions, the effect of the spatial neighbors depends on the transition variable that governs the regime switch. An LM test for detecting nonlinearity is derived, and a simulation study, where the properties of the test are investigated, is conducted. The test shows good power in relatively small samples with moderate deviation from linearity. An empirical application is included, where data on median house prices from the Boston area is used to explore the spatial dependence between census tracts. A smooth shift between regimes governed by the variable property tax rate is found. The smooth spatial lag model performs slightly better than the regular spatial lag model in terms of model fit and capturing spatial dependence.",1.0,https://doi.org/10.1177/01600176241237180
af4adff7998470e679ae436b0e2df6402a90ee46,10.1109/ecce64574.2025.11014018,Data-Driven Solutions for Human Trafficking: Detection and Impact Analysis Using Machine Learning,2025.0,European Conference on Cognitive Ergonomics,Nasrullah Masud; Abdullah Al Bassam; Abdus Sobhan; Md Tanvir Islam Sourav,"Human trafficking is a serious problem that requires new ideas and data-driven approaches to identify and solve. In this study, we analyze large datasets to identify patterns and anomalies in trafficking activity using powerful machine learning algorithms. In this task, we will use Random Forest. This powerful and versatile algorithm can be used for classification (e.g., spam detection, disease diagnosis) and regression tasks (e.g., house price prediction, stock market trends). It effectively handles missing values using surrogate splits and provides insights into feature importance, identifying influential predictors. The algorithm performs well with high-dimensional data, where the number of features is large compared to the number of observations, and can detect outliers and anomalies. Due to its ensemble nature, Random Forest is less prone to overfitting than individual decision trees. Automation in big data analysis continuously improves the efficiency and effectiveness of law enforcement activities, which, in turn, will translate into victim rescue operations and disrupt trafficking in most criminal networks. What is very important is ethical reviews on privacy, security, and",1.0,
7bcb8b3bbc9e3a09a3716c642441a58c44994ca6,10.17811/ebl.12.3.2023.186-202,The impact of home protection schemes on non-performing loans in Greece,2023.0,Economics and Business Letters,Andreas Zervas; Apostolos Fasianos; Konstantinos Loizos,"This paper explores the determinants of non-performing loans (NPLs) in Greece for the period 2002Q4 to 2018Q2, distinguishing between consumer, housing, and business loans. We contribute to the existing NPL literature by examining the impact of the home protection scheme, which has governed the Greek insolvency framework since the onset of the 2010 crisis. Consistent with previous research on NPLs, our findings indicate that the primary driver for all types of NPL classes has been the business cycle. Additionally, we uncover some evidence suggesting that the initial version of the home protection scheme may have contributed to non-repayment in specific NPL classes as property values declined. However, subsequent amendments to the scheme appear to have played a role in reducing NPL ratios, in addition to the positive effect of improving macroeconomic conditions. Our results offer valuable insights for policymakers seeking to address the burden of high NPL levels on Greece's economic recovery.    ",1.0,https://reunido.uniovi.es/index.php/EBL/article/download/18860/15927
dbe2948d5376907de536fb31d03bd93789812d82,10.37394/23205.2024.23.17,"Leveraging Data Mining, Machine Learning, and Web Scraping for Forecasting Rental Housing Prices in Tunisia",2024.0,WSEAS Transactions on Computers,A. Balti; M. Lakhoua; Mounir Sayadi,"The Tunisian real estate market has experienced a notable 13.5% surge in prices since 2018, marking a substantial departure from the preceding five years, during which there was a 9% growth, as indicated by data from the National Institute of Statistics (INS). According to the 2020 Rental Barometer, the average monthly rent for unfurnished apartments stands at 1,360 Tunisian dinars. Our initiative, titled ""Predicting Real Rental Prices,"" employs advanced machine learning techniques to provide accurate predictions for rental prices. Users of this platform can plan moves, organize properties into categories, and customize rental price insights based on their preferences. This project is based on machine learning and uses deep learning algorithms to predict rental prices, thereby meeting the needs of both lessors and tenants. The model ensures a thorough and accurate forecasting approach by accounting for a number of issues, such as the effect of furniture and building conditions on rental prices.",1.0,https://doi.org/10.37394/23205.2024.23.17
76591d68dd676906fcc0bb6c7e76c51b0e4f055e,10.1093/geronb/gbae066,Mortgage Borrowing and Chronic Disease Outcomes in Older Age: Evidence from Biomarker Data in the Health and Retirement Study.,2024.0,"The journals of gerontology. Series B, Psychological sciences and social sciences",Alec P. Rhodes; Stephanie Moulton; Cäzilia Loibl; D. Haurin; Joshua J Joseph,"OBJECTIVE
The medical diagnosis of a disease is common in older age and can carry significant financial costs. For many older adults, equity in a home is their primary component of wealth; however, housing wealth is illiquid. We analyze the relationship between the liquidation of housing wealth through mortgage borrowing on older homeowners' ability to successfully control a disease.


METHODS
We use data on homeowners age 65 and older from the 1998-2016 waves of the Health and Retirement Study (N=3,457). We use biomarkers and physical health indicators to measure disease control following a medical diagnosis of diabetes, heart condition, high blood pressure, lung disease, or cancer. Random effects linear probability and instrumental variable regressions estimate the associations of housing wealth, new mortgage borrowing, and disease control.


RESULTS
Descriptively, 28% of older homeowners who borrow against home equity are not controlled on their disease, compared to 33% of non-borrowers. Panel data instrumental variable regressions show that each $10,000 borrowed from home equity after diagnosis is associated with a 17 percentage-point reduction in the probability of the disease",1.0,
eea4dc71870988f4197b0622d678030e65c32bc7,10.4236/tel.2021.116068,"The Impact of Gambling Gross Income, Unemployment Rate, Money Supply and CPI on Residential Housing Value in Macau: Theory and Evidence",2021.0,Theoretical Economics Letters,Ting Lan,"This paper uses the classical and easy-to-understand Vector Auto-regression Model Method to study the impact of economic and monetary policy variables on the residential property market in Macau. By using sample monthly data from Jan. 2005 to May 2021, the empirical results show the dynamic relationship among the residential property value, key economic and monetary variables. Furthermore, adopting the Granger causality test, generalized impulse response function, variance decomposition and co-integration test of the corresponding VAR specification, the tables and graphs clearly explain the residential housing value has a positive relationship with the gambling gross income; it has a negative relationship with the unemployment rate; it has a positive relationship with the money supply; it has a negative relationship with the consumer price index. Finally, this paper gives five policy actions to help government stabilize the residential property market in Macau and make it healthy develop.",1.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=113107
496341a43811427014a9dcda0319cebcd0a98aa3,10.4314/jasem.v29i1.6,Evaluation of Price Prediction of Houses in a Real Estate via Machine Learning,2025.0,Journal of Applied Sciences and Environmental Management,A. Ibrahim; O. Ayilara-Adewale; A. Alabi; D. A. Olusesi,"Traditional (manual) methods of determining real estate house prices are in some cases prone to mistakes which may be due to distractions, lack of attentiveness or vulnerability to real estate agent fraud. This work focuses on evaluation house price prediction in real estate using more recent methods. House pricing using such methods as House Pricing Index and Random Forest Machine Learning Technique has been discussed, a new approach is proposed as a model utilizing the Extra Tree regression because it introduces an additional level of randomness in the tree-building process. Kaggle Boston housing dataset with 506 entries and 14 features was employed to train and test the developed model whose efficiency was then determined via mean absolute error and mean squared error. Additionally, a comparison was made between a random forest regression model and the proposed prediction model which revealed that the new prediction model yielded better performance than the random forest regression.",1.0,https://www.ajol.info/index.php/jasem/article/download/288025/271318
c53314afe50ea8b0557d58f6721f502db4ca1629,10.26504/qec2024win_sa_verma,Assessing expectations of European house prices,2024.0,,Akhilesh Kumar Verma; K. McQuinn,"Using a new database of consumers’ expectations, this paper examines the nature of house price forecasts across a select sample of European Union (EU) member states for the period 2020 to 2024. Across many EU countries, post COVID-19, house price increases have been apparent. Therefore, understanding the dynamics of house price movements is especially important at this time. In particular, we examine the rationality or otherwise of consumers’ house price expectations, and then examine the relationship between the expectations and forecasts of key fundamental determinants of house prices, such as interest rates and income levels. In this way we distinguish our work from most other studies of house price forecasts, which have not examined links between house price forecasts themselves and forecasts of the variables typically assumed to be determining prices. This is particularly relevant as oftentimes house price expectations themselves are influenced by changes in market fundamentals.",1.0,https://www.esri.ie/pubs/QEC2024WIN_SA_VERMA.pdf
b5a8b98a03bc42b7cdbaa84e59bf834a57df01c0,10.12775/equil2011.018,FEDERAL POLICY RESPONSES TO THE 2007-2009 CREDIT CRUNCH IN THE US,2011.0,,Łukasz Goczek,"This paper discusses the key characteristics of the U.S. financial crisis 2007-2009 and focuses on the Federal Policy Response to the lack of liquidity in the financial sector known as the “Credit Crunch”. The surprising depth of the crisis required unprecedented policy measures to be used in order to tackle the mounting liquidity problems in banks and prevent the subsequent credit crunch from taking its toll in the real economy. This required extension of monetary powers of the Federal Reserve and Treasury, which was unmatched in history. The policy response to credit crunch and house price bust was especially important given the fact that recessions following such events tend to be much deeper and longer than any other types of recessions. More importantly, however, the analyses of the current policy responses will determine which form financial markets will take in the next few decades, thus how vulnerable the world economy will be to next disruptions and liquidity problems.",1.0,http://economic-research.pl/Journals/index.php/eq/article/download/397/364
d5cf4474cfb10fc567f3625da0991c5d33732aa9,10.15849/ijasca.v18i1.30,Bitcoin Price Forecasting Leveraging X Data and Sentiment Indicators Via an LSTM-Enhanced Deep Learning Architecture,2026.0,International journal of advances in soft computing and its applications,Y. Özen; Mohammed Amen Azal Alwindawi,"The housing market is of great significance to the development and advancement of cities, but customary forms of property valuation are frequently biased, time-consuming, and not always effective. This paper focuses on the city of Irbid in Jordan, aiming to collect all the information on apartments and houses, predict the prices of properties, and clarify the key factors influencing the prices. Following the comprehensive cleaning process of the data and exploratory analysis, three ensemble machine learning models were trained and optimized to achieve accurate price predictions. The performance of all three models demonstrated excellent and consistent predictions, highlighting the efficiency of ensemble methods in predicting property prices. SHAP analysis indicated that the size of the house, the number of bedrooms, the number of lounges as well as the location are the most significant factors influencing the prices in Irbid. This reflects the functioning of the local market.",1.0,https://ijasca.zuj.edu.jo/index.php/IJASCA/article/download/30/29
53ea15fce9e460ecc4775eb8468116885f1da9b3,10.1109/hbdss54392.2021.00063,Research on Design Preferences of Furniture for the Elderly in the Home Care Model Based on Q Methodology,2021.0,2021 International Conference on Health Big Data and Smart Sports (HBDSS),Wei Chen,"The “aging of the population” is an inevitable topic in the development of modern society. As early as 1870, France, where the economy took off after the industrial revolution, had a population over 60 accounting for 12% of the total population, becoming the world's first elderly type nation. Since then, more than 50 countries including China, Sweden, Belgium, Germany, the United Kingdom, the United States, and Japan have also entered an aging society, and the issue of elderly care has also attracted much attention. In our country, most of them are only children, which can satisfy the care of life while maintaining a unique and relatively independent space. However, in the era of high housing prices, this model has certain requirements for the economic status of the family[1]. At present, most of the elderly living in our country choose welfare facilities such as traditional nursing homes, and most of them still choose home for the elderly. In the home life, furniture is closely related to the life of the elderly. Therefore, this research aims to investigate and analyze the preference of relevant personnel for the design of furniture suitable for the elderly under the home care mod",1.0,
b51ce3e749dfb9501d9d73fad1587e07a329c9b9,10.1109/ssaic61213.2024.00130,Research on Real Estate Sales Trend Prediction Based on Artificial Neural Network,2024.0,"2024 Asia-Pacific Conference on Software Engineering, Social Network Analysis and Intelligent Computing (SSAIC)",Hainian Zhang; Qian Chen; Benyu Zhao,"The sales price index of real estate is an effective tool to guide industry activities and market research, but the accuracy of prediction has always been paid more attention by people. With the continuous development of the real estate industry, the amount of data is getting larger and larger. Applying data mining technology to the real estate industry, processing and analyzing the sales data can truly reflect the value of the data. In this paper, taking the real estate data of a city in Jiangsu Province as an example, the quantitative research shows the modeling process. Based on ARMA model and the weighted least square method, the model is improved. Because the real estate market is a highly complex nonlinear system, the accurate description of the complex feature of house price trend is becoming more and more important. Artificial neural network is a new interdisciplinary subject. In recent years, it has been more and more applied to the prediction of practical problems, showing its broad application prospects. In particular, artificial neural network has great potential to predict the future behavior of nonlinear systems. BP neural network is applied to the real estate market ",1.0,
414567c350c28d32edccd04fa8c72f8847644043,10.55927/fjst.v4i2.20,House Price Prediction Using the Random Forest Algorithm on the Rapidminer Application,2025.0,Formosa Journal of Science and Technology,Fitria Fitria; Muhammad Syahid Pebriadi,"Home price prediction is an important aspect of the property industry, especially for real estate agents and potential buyers. With the advancement of technology, machine learning is used to improve the accuracy of home price estimates. One of the algorithms that is often used is Random Forest, which is able to capture complex patterns in data. However, previous research has shown that although this model has high accuracy (high R²), prediction errors are still significant (RMSE and MAE are high), indicating features that have not been fully modeled well. This research uses property datasets, preprocessing (normalization and feature selection), and applies Random Forest in RapidMiner. The model was evaluated using RMSE, MAE, and R², which showed R² = 0.78, but with RMSE = 4,757,343 and MAE = 3,200,000, indicating a large prediction difference. The most influential features are the area of the building (40%), the quality of the house (25%), and the number of bathrooms (10%).",1.0,https://doi.org/10.55927/fjst.v4i2.20
a0e49e6348f59aac8fc321beb7cf18a780bbb911,10.48550/arxiv.2507.01347,Learning from Random Subspace Exploration: Generalized Test-Time Augmentation with Self-supervised Distillation,2025.0,arXiv.org,Andrei Jelea; A. Belbachir; Marius Leordeanu,"We introduce Generalized Test-Time Augmentation (GTTA), a highly effective method for improving the performance of a trained model, which unlike other existing Test-Time Augmentation approaches from the literature is general enough to be used off-the-shelf for many vision and non-vision tasks, such as classification, regression, image segmentation and object detection. By applying a new general data transformation, that randomly perturbs multiple times the PCA subspace projection of a test input, GTTA creates valid augmented samples from the data distribution with high diversity, properties we theoretically show that are essential for a Test-Time Augmentation method to be effective. Different from other existing methods, we also propose a final self-supervised learning stage in which the ensemble output, acting as an unsupervised teacher, is used to train the initial single student model, thus reducing significantly the test time computational cost. Our comparisons to strong TTA approaches and SoTA models on various vision and non-vision well-known datasets and tasks, such as image classification and segmentation, pneumonia detection, speech recognition and house price prediction, ",1.0,
7ce0ce0a1a351c3be6e43b20702afad3ceeada7c,10.26595/eamr.2014.5.2.1,"Seasonality, Infrastructures and Economic growth in Touristic islands",2019.0,European Accounting and Management Review,Juan Pedro Aznar; Josep Masria Sayeras Maspera; Jordi Vives,"Tourism is the main economic activity in some small islands that have became very dependent on this activity. Sun and beach destinations are characterized by high levels of seasonality, with consequences on unemployment, economic activity diversification and GDP per capita. This paper analyzes the empirical evidence from the Balearic island of Mallorca, one of the most important touristic destinations in Spain. This research has analyzed the existence of correlation between seasonality and unemployment, GDP per capita, housing prices, the paper also examines the importance of location and infrastructure. The variable with a highest explanatory capacity in terms of GDP per capita is the distance to the airport. National and regional governments must consider the importance of infrastructures and the role seasonality plays in economic growth and economic activity diversification; the understanding of these relations will help to develop the most adequate public policies.",1.0,https://doi.org/10.26595/eamr.2014.5.2.1
f49dedbf7b2f17cb72917a83bf82eab9adeeab59,10.24014/sitekin.v22i2.35804,Property Price Prediction Using the Random Forest Regression Algorithm,2025.0,Jurnal Sains dan Teknologi Industri,P. Utami; Muhamad Jundi; Rahmaddeni Rahmaddeni; L. Sinaga,"This study uses an innovative approach to predicting property prices using machine learning with the Random Forest Regression method. The dataset was obtained from Kaggle and consists of 500 rows with 12 attributes, comprising 10 numerical attributes and 2 categorical attributes. The evaluation results, calculated using the R² score on the test dataset, show strong performance, achieving the highest R² score of 81.88% with a dataset split ratio of 90:10. The scatter plot visualization indicates shows the model's predictions often correspond closely with the actual values, showing strong accuracy, despite a tiny gap between the anticipated and real values. The graph comparing the training data and the actual data shows no significant signs of overfitting or underfitting, demonstrating the Random Forest Regression model's strong accuracy in predicting house prices and its capacity to effectively capture the relationship between independent and dependent variables.",1.0,https://ejournal.uin-suska.ac.id/index.php/sitekin/article/download/35804/12258
b1b619e720e2b99c46068570f3d9d3b944589bb0,10.2478/remav-2022-0006,On Place Safety,2022.0,Real Estate Management and Valuation,T. Kauko,"Abstract For a long time, social factors have been identified as a set of crucial determinants of residential location choice and property value. Here, safety and security issues constitute a significant issue. Inhabitants are traditionally concerned about their neighborhoods and housing locations, and, considering current problems in big Western cities, this concern is by no means lessening. The study presents a social innovation for assisting the search for safe housing environments. A list of quantifiable key features regarding negative externalities and actual criminality provides the basis for constructing a checklist for the comparison of safe places within a city, or comparing cities with respect to their safety for housing occupants, investors, developers and other stakeholders. The controversial nature of the argumentation notwithstanding, this method is suggested to prove valuable in circumstances marred by increasing social hazards and turmoil.",1.0,https://www.sciendo.com/pdf/10.2478/remav-2022-0006
1165fd19265663d89986a4cf836d053c46a71996,10.1007/s11146-021-09836-2,Ignoring Spatial and Spatiotemporal Dependence in the Disturbances Can Make Black Swans Appear Grey,2021.0,Journal of real estate finance and economics,R. Pace; R. Calabrese,"Automated valuation models (AVMs) are widely used by financial institutions to estimate the property value for a residential mortgage. The distribution of pricing errors obtained from AVMs generally show fat tails (Pender 2016; Demiroglu and James Management Science, 64(4), 1747–1760 2018). The extreme events on the tails are usually known as “black swans” (Taleb 2010) in finance and their existence complicates financial risk management, assessment, and regulation. We show via theory, Monte Carlo experiments, and an empirical example that a direct relation exists between non-normality of the pricing errors and goodness-of-fit of the house pricing models. Specifically, we provide an empirical example using US housing prices where we demonstrate an almost perfect linear relation between the estimated degrees-of-freedom for a Student’s t distribution and the goodness-of-fit of sophisticated evaluation models with spatial and spatialtemporal dependence.",1.0,https://link.springer.com/content/pdf/10.1007/s11146-021-09836-2.pdf
b0c1fb4826e41db24d5926154604901bd02c7423,10.54254/2754-1169/77/20241802,Influential Factors on California Regional Housing Price Analysed by Multiple Linear Regression,2024.0,"Advances in Economics, Management and Political Sciences",Yanqiang Cao,"While metropolises keep expanding with the increasing population in recent decades, the need for housing rises inevitably. This paper aims to find the most explanatory factors for the housing price in California; with 20433 entries of data found in Kaggle, the method of multiple linear regression (MLR) is applied to find the most influential factors. 500 entries in the dataset are chosen randomly and are divided into 2 datasets for training and testing purposes. Models have been developed in R by using the training dataset. After comparing the adjusted R square and variability of the models, the most convincible model will be selected to find out the result of this investigation on the test dataset. After model diagnostics, the result of this analysis is that the regional median income level has a strong positive correlation with the housing price, and it is the most influential factor. Other influential factors will be introduced in the conclusion.",1.0,https://www.ewadirect.com/proceedings/aemps/article/view/11435/pdf
6d43c92115595a6e9320d3384d63529c7c8e7d3d,10.35532/jsss.v2.015,Does urbanization promote rising house prices?,2018.0,,Yunyun Luo,"With the acceleration of urbanization process and fluctuations in real estate prices, housing problem has become an urgent problem that hinders the improvement of people's living standards. Based on the panel data of 31 provinces (excluding Hong Kong, Macao and Taiwan) in China from 2001 to 2016, this paper established a spatial econometric model to analyze the impact of urbanization on housing prices. It found that there are regional differences in the impact of urbanization on housing prices. there are obvious positive effects in the eastern and central regions, negative effects in western regions, and no significant effects in northeast China. In addition, the current house price is also affected by factors such as the previous house price, per capita GDP, and household consumption level. This study is helpful to understand the relationship between urbanization level and housing prices, and maintain the stable estate prices in various regions.",1.0,https://aeescience.org/uploads/ppapers/2019092701APFESD2019/ESD92715.pdf
85884e2de28869e46d9a7e5971591e70b40e44d1,10.3846/ijspm.2022.16970,IMPACT OF MAJOR EVENTS ON HOUSING PRICES IN HANGZHOU BASED ON REGRESSION DISCONTINUITY,2022.0,International Journal of Strategic Property Management,Ling Zhang; Lifei Zhu; L. Yang,"Based on the regression discontinuity method, housing transaction price data of Hangzhou at the community level were used to assess the impacts of a serious of major events on housing prices in Hangzhou. The results show that the 2008 financial crisis had the strongest negative impact on housing prices, with prices decreasing by about 20%. Winning the right to host the G20 summit did not significantly impact housing prices, while holding the summit caused an increase in prices (about 10%). The first implementation of the home purchase restriction (HPR) caused a decreasing discontinuity of housing prices. Cancellation of the HPR did not cause a discontinuity effect, but the trend changed from negative to positive, and resumption of the HPR for local residents yielded the largest drop in housing prices, resulting in a 10% downward cut-point. However, the impact time was relatively short and did not change the rapid rising trend of housing prices.",1.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/16970/11187
85d857380079e0586bf9e2de77b6bf63518cc948,10.32609/0042-8736-2025-1-92-114,Macroeconomic factors of housing prices in Russia,2025.0,Voprosy Ekonomiki,G. Lysenko,"The main household decisions are related to the consumer and housing market, while housing decisions are related to mortgages. Moreover, both markets are significantly influenced by the macroeconomic environment. The paper aims to study the structural relationships between the macroeconomic environment, consumer and housing markets. How do these aspects relate to each other in Russia? To analyze the associations, the Bayesian vector autoregression methodology with sign restrictions is used. The obtained results show that the housing market strongly depends on fluctuations in the consumer market, whereas the inverse dependence is not so strongly manifested. At the same time, the impact of mortgage shocks and monetary conditions has a greater impact on the volume indicators of the consumer and housing markets than on prices. The dynamics of housing prices is more related to the supply of housing, while consumer prices are related to the ruble exchange rate and supply and demand shocks. The impact e of oil prices on the dynamics of both markets is analyzed. Oil prices have the most significant impact on household consumption, mainly through the demand channel. One way of functioning o",1.0,
3c9caf715802e4b04d9f81994bbb50af4c642127,10.1108/jerer-02-2022-0004,Drivers behind the accuracy of self-reported home valuations: evidence from an emerging economy,2022.0,Journal of European Real Estate Research,Mateusz Tomal,"PurposeThis paper aims to explore the drivers behind the accuracy of self-reported home valuations in the Warsaw (Poland) housing market.Design/methodology/approachIn order to achieve the research goal, firstly, unique data on subjective residential property values estimated by their owners were compared with market-justified ones. The latter was calculated using geographically weighted regression, which allowed for taking into account spatially heterogeneous buyers' housing preferences. An ordered logit model was then used to identify the factors influencing the probability of the occurrence of bias towards over or undervaluation.FindingsThe results of the study revealed that, on average, homeowners overvalued their properties by only 1.94%, and the fraction of interviewees estimating their properties accurately ranges from 20% to 68%, depending on the size of the margin of error adopted. The drivers of the valuation bias variation were the physical, locational and neighbourhood attributes of the property as well as the personal characteristics of the respondents, for which their age and employment situation played a key role.Originality/valueIn contrast to previous studies, this ",1.0,
93190686f11cdfca6207ea210add39da132cb244,10.1109/icacc63692.2024.10845250,Leveraging Hybrid Machine Learning Technique For Effective House Rental Price Forecasting,2024.0,2024 11th International Conference on Advances in Computing and Communications (ICACC),K. D; Naveenkumar Anbalagan; M. M; Najma N; K. K; M. P,"These days, figuring out the house's sale price is crucial because both the land and the house's prices rise annually. Therefore, the next generation needs a simple approach for forecasting future rents for houses. Accurate and data-driven strategies are crucial for estimating house rental prices in the rapidly shifting real estate market. This study investigates the application of machine learning to accurate rental price forecasting. Conventional methods of rental estimation tend to rely on intuition and knowledge of the marketplace in the area, but they are incapable of taking into consideration the complex network of variables which influence rental rates. This study explores how rental prices are influenced by factors including the age of the property, whether it is furnished, the flat area, the size of the house etc. It also shows how machine learning models may be used to find hidden connections and get precise rental price predictions. The dearth of highly accurate models currently in use, the need to reduce overpricing, adjust to changing conditions in the market, maximize the return on investments, support data-driven decision-making, advance market transparency, and crea",1.0,
4fe1edbcedb401433d121acfaca6b564e2e54912,10.2991/amsm-16.2016.104,"Spatial Structure of Housing Prices in Nanchang, China",2016.0,,Li-Ting Xu; Ye-Qiao Wang; Yu Xu; Yu Zhou; Zengzheng Wang,"Spatial variation of housing prices is an important contents of urban geography studies. Most efforts have studied space structure of residential district in big cities. The aim of this study was to apply it in a medium-sized city, Nanchang. Spatial autocorrelations analysis and Kriging interpolation method were used to explore the spatial distribution of residential district. Residential district data were based on 808 residential districts collected from two real estate websites at June 2015. In this transverse study, housing prices shows significant positive spatial correlation with significant characteristics of ""shortcut"" and ""hydrophilic"". Moreover, the spatial distribution of housing prices is unbalanced, which shows the feature of one major center and multiple sub-centers. Future research may benefit from additional longitudinal studies to confirm these findings. Keywords-residential district; housing prices; spatial pattern",1.0,https://www.atlantis-press.com/article/25856445.pdf
afd23eb66b586a85d873581cd4d707be5252f2a0,10.2148/benv.50.1.25,Neighbourhoods still Matter Because Housing Market Actors Believe They Matter,2024.0,Built Environment,George C. Galster,"The urban neighbourhood – though increasingly assailed as a problematic spatial construct – still matters in contemporary societies because it is a crucial unit of reference in metropolitan housing markets. The logic is as follows. Key housing market actors – households,
 owners, developers, and agents – believe that the local area in which they live, own property, or try to sell property influences their own wellbeing for a number of social-psychological and/or economic reasons. Because these people then act on their neighbourhood-related beliefs in
 a variety of ways, this implies that neighbourhoods are important: they generate collective behavioural responses. Put more succinctly, neighbourhoods must still matter because housing market actors believe they matter and then behave based on these beliefs. Evidence to support this logic
 is assembled here from a wide variety of interdisciplinary sources. For households, we observe the strong relationship between neighbourhood conditions and satisfaction, housing search, and intra-urban mobility. For property owners and developers, we observe how neighbourhood attributes strongly
 influence property values and, relatedly, the virulen",1.0,
7c8035c6809554a64fd4842c09d249fe44aa20d2,10.54254/2754-1169/6/20220155,House Price Prediction with Big Data,2023.0,"Advances in Economics, Management and Political Sciences",Ziyu Cui,"Many of the current models for house price prediction focus on the house price index itself or include too few factors. In this research, however, different aspects of housing properties (bedrooms, bathrooms, grade, view, etc.) will be incorporated to generate a more accurate prediction model. Doing so not only remedies for a lack of variables in current models but also benefits the consumers by providing an accurate estimation of housing values. This paper examines the estimation methodology of a multiple linear regression model. To get the optimal prediction power and keep the model as simple as possible, different variable combinations will be tested. Through comparing different regression models and analyzing the regression results, the predictive model introduced in this paper has a high house price prediction power. This paper provides a potential solution for the prediction of the house price in the King County, Washington.",1.0,https://aemps.ewapublishing.org/media/88a0814964e743b089342a23b26b9653.marked_M7pRCRv.pdf
f61af9182b02e88d64f0b234e7e4dbdeea13a001,10.1371/journal.pone.0330932,The spatial dynamics of urban vegetation and housing prices: Insights from pre- and post-pandemic Chicago using OLS and MGWR models,2025.0,PLoS ONE,D. Han; Chang Gyu Choi,"This study examines the spatial dynamics of urban vegetation and its impact on housing prices in Chicago, analyzing data from both pre- and post-COVID-19 periods. Employing Ordinary Least Squares (OLS) and Multiscale Geographically Weighted Regression (MGWR) models, we assess how the effects of green spaces on property values vary across different neighborhoods. The OLS model generally indicates a positive correlation between increased vegetation and housing prices. In contrast, the MGWR model reveals that the benefits of urban green spaces to property values are not uniformly distributed and exhibit significant variability. Notably, in some South Side areas of Chicago, increases in green space correlate with declines in property values, a sensitivity that intensified post-pandemic, leading to notable price declines. Conversely, the North Side, characterized as a higher-income area, shows greater resilience to the impacts of both increased green spaces and the COVID-19 pandemic, with less susceptibility to economic downturns. This research underscores the intricate interplay between urban green spaces and economic factors, highlighting how local socio-economic conditions and urban ",1.0,
4c5e35bb2c2eecfdae466de3e69408e994ba4c75,10.55057/ijarih.2022.4.2.1,A Conceptual Paper on Factors Affecting the Behaviour of Muslim Generation Y Towards House Acquisition in Malaysia,2022.0,International Journal of Advanced Research in Islamic and Humanities,,"House is the most important basic need for the human survival, however many people especially those who are categorised in the category of generation Y do not acquire a house due to internal and external factors. In accordance, the volume of residential transaction and homeownership rate among youths in Malaysia has been decreasing for the past few years. It is proven as the number of residential property transaction has dropped from 272,669 transactions in 2012 to 198,812 transactions in 2021. Thus, it is important to examine the behavior of generation Y towards house acquisition in Malaysia. The research proposes to examine the internal factors (attitude) and external factors (house feature/attributes, house location, financing factors and house price) in influencing house acquisition behavior among Muslim generation Y. As such, this study proposes to adapt Attitude-Behavior-Context (ABC) theory in discovering the behavior.",1.0,https://myjms.mohe.gov.my/index.php/ijarih/article/download/18595/9835
4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,10.62051/cs0pd728,Research on the Influencing Factors of Rental House Prices,2024.0,"Transactions on Economics, Business and Management Research",Xiaoyan Mao,"Since the 1990s, China's real estate market has gradually recovered, and has developed into a large and mature trading market today, as the housing price increase, the demand of housing rental also increases a lot. Based on this this article aims to identify the factors that have an impact on rental price. The method of multi-linear regression is used to analyze the factors with over 10,000 samples in Guangzhou from May 2020 to June 2020 from the website of LianJia, one of the biggest real estate agencies in China. The dataset contains 10 variables in total, this research uses VIF, Pearson correlation coefficient to narrow the variables down to District, Rent Type, Area, Elevator Flag and Buld Floor these five variables and considered that they have a significant linear relationship with rental price. By focusing on these variables this study provides a view of the relationship of demand and supply of China’s renting market.",1.0,https://wepub.org/index.php/TEBMR/article/download/3071/3355
dc11cbfaccfc2b053ca030b6c7e8031ad2bfa026,10.36407/jmsab.v5i2.626,Pricing strategy and buyer's considerations for house: Central government area vs tourism centre area,2022.0,Jurnal Manajemen Strategi dan Aplikasi Bisnis,Renna Magdalena; Oliandes Sondakh,"The purpose of this study is to propose Pricing Strategy. First, this study determines the dominant factors that influence the number of housing prices in Denpasar as the Capital city of Bali Island, a center of tourism in Indonesia and Jakarta as the Capital City and central government of Indonesia. This study uses a multiple linear regression test; the sample was 115 houses data, 60 houses in Denpasar, and 55 in Jakarta. Second, this study will propose a strategy for determining house prices. The survey results will show what factors are dominant in determining the number of housing prices in Denpasar and Jakarta. This research is helpful for home sellers to determine the cost of the house to be sold. This research is also helpful for prospective home buyers to determine the reasonableness of the house price they will buy. This study also covers the discussion regarding the consideration of homebuyers in terms of taxes.",1.0,https://doi.org/10.36407/jmsab.v5i2.626
a658f1206c981ca29b7dee7dec99dc7b24b457ee,10.1016/j.cities.2024.105492,Investment and crime on residential street segments: Assessing the moderating role of neighborhood characteristics,2024.0,Cities,Marie Skubak Tillyer; Arthur Acolin; Rebecca J. Walter,"Research suggests that investment in the form of structural home repairs, new construction, and home renovation is associated with crime reductions in residential areas. The current study examines how the relationship between investment and crime on residential street segments is moderated by neighborhood characteristics using longitudinal data from six large U.S. cities to better understand how investment strategies can be targeted to maximize public safety benefits. We estimate spatial Durbin models with street segment and census tract by year fixed effects to examine the effects of one measure of direct investment, building permits, on changes in crime at street segments and the extent to which such effects vary by census tract-level concentrated disadvantage, residential stability, median housing values, percent non-White, percent foreign-born, and population density. Findings reveal a significant negative relationship between permit activity and changes in crime in all cities. With few exceptions, effects were strongest among segments in the densest neighborhoods with the highest percentage of foreign-born residents where concentrated disadvantage is high and residential stabi",1.0,
31361244b2b00e040d7debcd55672569e95554dd,10.20955/r.96.147-172,Representative neighborhoods of the United States,2014.0,,A. Badel,"Many metropolitan areas in the United States display substantial racial segregation and substantial variation in incomes and house prices across neighborhoods. To what extent can this variation be summarized by a small number of representative (or synthetic) neighborhoods? To answer this question, U.S. neighborhoods are classified according to their characteristics in the year 2000 using a clustering algorithm. The author finds that such classification can account for 37 percent of the variation with two representative neighborhoods and for up to 52 percent with three representative neighborhoods. Furthermore, neighborhoods classified as similar to the same representative neighborhood tend to be geographically close to each other, forming large areas of fairly homogeneous characteristics. Representative neighborhoods seem a promising empirical benchmark for quantitative theories involving neighborhood formation.",1.0,https://files.stlouisfed.org/research/publications/review/2014/q2/badel.pdf
f68fd42aced280f4e2b1576620c43e4e1470d159,10.1145/3615522.3615542,"Explore, Compare, and Predict Investment Opportunities through What-If Analysis: US Housing Market Investigation",2023.0,International Symposiu on Visual Information Communication and Interaction,Hongruyu Chen; Fernando Gonzalez; Oto Mraz; Sophia V. Kuhn; Cristina Guzman; Mennatallah El-Assady,"A key challenge in data analysis tools for domain-specific applications with high-dimensional time series data is to provide an intuitive way for users to explore their datasets, analyze trends and understand the models developed for these applications through human-computer interaction. To address this challenge, we propose a three-stage workflow that allows domain experts to explore their data, compare the different entities’ features, and predict the variable’s long-term trend using what-if analyses. Based on this workflow, we created a data visualization workspace for real estate investment using data from the US housing market at state and city level. The underlying machine learning model ARIMAX uses house price data together with socio-economic data from 2000 to 2021 to learn the dependencies of the house prices on the socio-economic factors and make informative and robust predictions for future years.",1.0,https://dl.acm.org/doi/pdf/10.1145/3615522.3615542
7dd6f1e69a7a0886c972c7966f758bc8e289253c,10.21511/imfi.16(3).2019.05,Auto-regressive Distributed Lag Model for long-run US household debt determinants,2019.0,Investment Management & Financial Innovations,E. Swanepoel,"US household debt increased on a yearly basis from 1987 to 2007. In addition, household debt in the USA nearly doubled between 2000 and 2007, from $5.6 trillion to $9 trillion. This came to an abrupt end in 2009 with the crash of the financial market. This paper employs the bound test and Auto-regressive Distributed Lag Model to determine the long-run relationship between US household debt and consumer prices, housing prices, the unemployment rate, and the lending rate. Unit root tests were conducted first to ascertain the stationarity of the variables. E-views 11 was used in the analysis of the data, which was obtained from Q1: 1990 to Q1: 2007 from the International Monetary Fund and the US FED. It was found that in the long run, there is a negative effect of consumer prices and unemployment on US household debt, while house prices and the lending rate would have a positive effect on household debt.",1.0,https://businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/12330/IMFI_2019_03_Swanepoel.pdf
c66f5c787afea2e3a2b8b378cc2188e6013ee22f,10.54097/hbem.v21i.14755,Feature Selection in House Price Prediction,2023.0,"Highlights in Business, Economics and Management",Jia Guo,"This study aims to construct a model to choose effective features and use them to predict the market price of an exact house, which can help people with house pricing and property evaluating. This paper preliminarily constructs several machine learning models like Linear Regression, SVM, KNN and compares their accuracy on this problem to choose the best-fit one for improving. After using parameter tuning to optimize this model, this study tries to use and recursive feature elimination and genetic algorithm to select features to improve simple SVM model. After feature selection, this study re-evaluated the accuracy of the model and compared which features had a greater impact on the predictions. After comparison, this study found that in this particular case, features have closer connections with living condition, traffic condition and sale condition will have a huge impact on the house price.",1.0,https://drpress.org/ojs/index.php/HBEM/article/download/14755/14302
83fd3fee5484e20aba8c9cf1a788ef885495821b,10.56028/aemr.6.1.523.2023,Monetary policy and housing bubble,2023.0,Advances in Economics and Management Research,Yue Gao,"This research paper examines the relationship between monetary policy and housing prices in macroeconomics, with a focus on the United States. Housing prices play a substantial role in the consumer price index, and therefore, ensuring price stability for goods and services. The paper reviews the literature on two different perspectives: John Taylor's argument that loose monetary policy contributed to a surge in house prices, and Ben Bernanke's perspective that other factors caused the housing bubble. This paper contains the discussion on the literature of monetary policy and house prices, using the linear regression model and the idea of Taylor rule from different perspectives to identify the relationship between monetary policy and the housing prices. The analysis reveals differences in purposes of the models, which provides a basis for further discussion on the rule of monetary policy.",1.0,https://madison-proceedings.com/index.php/aemr/article/download/1367/1362
367887ce2c655ba1fc1da17180adc3b9c3f1858c,10.24193/rvm.2025.18.124,Artificial intelligence in Automated Valuation Models for real estate sector,2025.0,Virgil Madgearu Review of Economic Studies and Research,Silviu-Ionuț Băbțan,"Real estate valuation objectively assesses a property’s value and is the foundation for leasing, selling, and buying real estate. This study incorporates fundamental elements and new insights into the process of valuing residential properties using Automated Valuation Models (AVMs) enriched with Artificial Intelligence (AI). In the swiftly developing real estate market, implementing AVMs is inevitable. This study aims to identify the key techniques for AVMs, analyse their worldwide implementation, and assess their application within different real estate sectors. By examining these aspects, we aspire to offer a holistic insight into how AVMs revolutionize property valuation procedures and their wider significance for the real estate market. By employing innovative analytical methods, including AI, the paper focuses on building a more accurate and efficient process of property valuation.",1.0,https://doi.org/10.24193/rvm.2025.18.124
55423d61dd1b3f0bf993da53a1dc1b1951698e74,10.12973/ejmste/77952,"Heterogeneous Demand, Government Intervention and Housing Price: Chaos Analysis Perspective",2017.0,,Qian Sun; Jichang Dong; Yong Tang,"Started from the microbodies of real estate market, this paper supposed real estate price is mainly determined by the bodies’ market behaviors and related interactions, while the demanders in real estate market are heterogeneous. and local government and central government are with different purposes in regulating the market. For these considerations above, this paper constructed a real estate price deterministic model, and conducted a numeric simulation. The main findings include: the price fluctuation of real estate market has chaotic characteristics, and the systematic parameters have a comparatively big effect on market equilibrium. The sensitivity that both consumption demanders and real estate developers have on real estate price is positively correlated to real estate market equilibrium. In the long term, the price of real estate market will be approaching to its actual value.",1.0,https://doi.org/10.12973/ejmste/77952
50cd1d6d89ad6aec7466ffeb9b54a8ce685d6e9c,10.18055/finis4209,Spatial dimension changes in second hand housing prices in Alcalá de Henares and León (Spain),2014.0,,María Jesús González González; María Luisa de Lázaro Y Torres,"The objective of this study is to show different recent trends (2001-2009) in second-hand housing prices in Spain and in different neighbourhoods in Alcala de Henares and Leon, both of which are cities with very diverse economic and demographic characteristics. The first is a city in the metropolitan area of Madrid, with high landprices. The increasing demand for housing in Alcala de Henares is a good alternative for households, in view of high prices in Madrid. The second is Leon, the capital of the province of Leon, which is undergoing a depopulation process. We will demonstrate that house price dynamics is a local phenomenon and national or regional level data conceal interesting differences within cities (districts and neighbourhoods). The latest rise and decline in housing prices is clearly visible on the outskirts and sometimes non-existent in the town centre area.",1.0,https://doi.org/10.18055/finis4209
ed09f6866fe64aaec6f23c271b81286ead443347,10.1109/igarss47720.2021.9554959,Urban Residential Land Price Assessment Based on Transfer Learning,2021.0,2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS,Weishi Jin; Mingcang Zhu; Yong He; Jie Li; Zezhong Zheng; Ming Feng; Zhong-Nian Li; Zhiyong Wang; Ming-Qiu Li; Linghai Jiang; Qiang Liu; Ankai Hou; Biao Zhang; Xuemei Li,"With the development of urbanization in China, the land economy accounts for more and more percentage in the gross domestic product (GDP) of China. Therefore, the accurate assessment of urban residential land price is of great interest for the governments. In the paper, we introduced the transfer learning to assess the urban residential land price, taking the Shenzhen city in China as a case. First, we collected housing price, land price data and points of interest (POI) data. The POI data was quantified as the influencing factors of two price data. Second, we used housing price data and influencing factors to train a high-accuracy model based on deep belief network (DBN). Third, we kept the DBN model unchanged, and then used three new models connected in order to better assess land price. The three models are back propagation (BP) neural network, support vector machine (SVM), and random forest (RF). Finally, we used land price data and influencing factors with three models to find the best one. The models were trained by the method of five-fold cross validation. Our results showed that all of three transfer learning models had good accuracy but the model with random forest was mor",1.0,
2b4d204e6766efd485e5733f0afbdc042d85f0cb,10.36883/jfres.v3i2.48,"Pengaruh Lokasi, Pendapatan Konsumen, Harga Rumah Dan Selera Terhadap Keputusan Pembelian Rumah Bersubsidi Di Kabupaten Manokwari",2020.0,JFRES: Journal of Fiscal and Regional Economy Studies,Noviani Walalayo; Rumas Alma Yap; L. M. Orisu,"This study aims to determine the influence of location, consumer income, house prices, appetite for subsidized home purchase decisions in Manokwari Regency. The variables in this study are Location, Consumer Income, House Price and Taste. This study uses primary data and secondary data. Primary data obtained through questionnaires and secondary data obtained through agencies related to this study, the analysis method used is to use multiple linear regression models using the SPSS Statistic 16 program. The results of the partial study (t test) Location, Kosumen Income, House Prices and tastes had a positive and significant effect on the decision to purchase subsidized homes in Manokwari Regency. But together (simultaneously) variable locations, consumer income, house prices and tastes significantly influenced purchasing decisions at a confidence level of 95 percent.",1.0,http://jfres.unipa.ac.id/index.php/jfres/article/download/48/73
2e14fafc9e252e40d7b977a06c91db6619aac698,10.15396/eres2019_136,The Influences of Housing Prices on Residential Mobility and Unemployment,2019.0,26th Annual European Real Estate Society Conference,Chien-Wen Peng; I. Tsai,"A change of housing price has great impact on households’ housing equity and further moving decision. Some previous studies reveal that whether owning a home is an important factor of residential mobility, and a declining housing price may cause mobility lock-in effect and higher unemployment (Henely1998; Hsieh et al. 2003; Zabel 2012; Blozea and Skak 2016). However, some other studies find that the influences of housing price on residential mobility and unemployment are not significant as expected (Engelhardt 2003; Coulson and Grieco 2013; Vallentta 2013) or the influences are only significant in a smaller regional level but not significant in a national level (Modestino and Dennet 2013). This study reexamines their relationship by using panel cointegration method and the city level panel data during 1994 and 2017 in Taiwan. The empirical results reveal that housing price are cointegrated with migration and unemployment, and the influences of housing price on migration and unemployment are significantly positive and negative respectively in the long run. However, the influences of housing price on migration and unemployment are not as significant as expected in the short run. We u",1.0,
2794fe93cf1856116aead15e9624918e942285ad,10.12794/metadc84266,Gentrification in Oklahoma City: Examining Urban Revitalization in Middle America,2011.0,,C. Petty,"Gentrification applies not only to the largest and oldest cities; it is a multi-scalar phenomenon playing out in smaller and less prominent settings as well. This study examines temporal changes in property values, demographic characteristics, and types of businesses in the central Oklahoma City area. A major urban revitalization project which began in 1993 created strong gentrification characteristics near the renewal's epicenter, the Bricktown entertainment district. Data suggest that several specific neighborhoods in the surrounding area exhibited rising property values, improving educational attainment rates, decreasing household sizes, and a shift toward cosmopolitan retail activity. While it is evident that Bricktown has been transformed, the socio-economic traits of surrounding neighborhoods have been altered by the ripple effects of urban renewal.",1.0,https://digital.library.unt.edu/ark:/67531/metadc84266/m2/1/high_res_d/thesis.pdf
8378f1d912a57b54da70a61e0f005d48001a3408,10.1177/14782103251381543,"Refugees, students, the housing shortage in Australia: Examining the correlation",2025.0,Policy Futures in Education,Hasham Khaleel AL MUSAWI,"Australia’s housing shortage is a multifaceted socio-economic challenge marked by escalating property prices and a critical shortage of rental accommodations. This article examines the interplay of factors contributing to the crisis, including systemic inefficiencies in policy, construction sector challenges, and population dynamics. While public discourse often attributes the housing shortage to the influx of refugees and international students, this analysis reveals that these groups, representing a small fraction of the population, are not the primary drivers of the crisis. Instead, delays in housing approvals, workforce shortages in the construction industry, and rising material costs emerge as significant contributors. Through a critical examination of existing data and policy frameworks, the article highlights the need for targeted solutions. Strategies such as streamlining bureaucratic processes, investing in social and affordable housing, addressing labour shortages, and adopting responsive urban planning are proposed. The discussion underscores the importance of avoiding scapegoating vulnerable populations and instead fostering inclusive, sustainable policies to resolve th",1.0,
4b2486febbcf1067bb316559cd8185fb5a45e9a0,10.54254/2754-1169/43/20232154,Application of Machine Learning in Boston House Price Prediction,2023.0,"Advances in Economics, Management and Political Sciences",Yuanheng Zhang,"House prices are an important economic indicator for a country or region, rising house prices are often associated with economic growth and increased employment opportunities, while a decline in house prices may indicate an economic slowdown or other unfavorable factors. Governments and relevant agencies need to understand the dynamics of housing prices to formulate appropriate housing policies and plan urban development. As one of the major economic centers in the United States, the fluctuations in Boston's housing prices can reflect the local economic conditions and development trends. This paper selects data from Boston in 1970. Each record in this database describes a Boston suburb or town. The proposed method was evaluated using 5 metrics. By comparison, the paper ends up with the result that XGBoost works best out of the four regression models.",1.0,https://www.ewadirect.com/proceedings/aemps/article/view/6223/pdf
52306d5d3cbbd9af7273df88dad689e6309a38bd,10.36448/jsit.v10i1.1216,SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN PERUMAHAN DI BANDAR LAMPUNG,2019.0,Explore: Jurnal Sistem informasi dan telematika,H. Yunita; Fatimah Fahurian,"Houses are one of the basic human needs besides clothing and food. There are many factors that influence everyone to choose a house to live in, including a house model, house prices, land area, building area, distance to location, environment and so on. In Bandar Lampung, there are many housing estates. But the absence of a system that can be used by the community as prospective buyers to choose housing to be purchased in accordance with the desired criteria.  This has resulted in consumers, especially buyers of houses or housing, still finding it difficult to find housing according to their desired and financial needs. In this study a housing decision support system was applied. And the results of decision support systems can help decision makers, namely buyers in choosing housing that fits the desired criteria and according to needs and finances.",1.0,http://jurnal.ubl.ac.id/index.php/explore/article/download/1216/1376
f752e72dff96f69cbda6ded7247913f1310cd941,10.1080/02664763.2020.1840534,Determinants of the heavily right-tailed residential housing price in Tianjin,2020.0,Journal of Applied Statistics,B. Zhao; Ru Su,"The housing price in Tianjin, one of the typical monocentric cities of China, exhibits a heavily right-tailed distribution even after the logarithm transformation of the price, which might lead to a biased estimation of the parameters under normal distribution assumption. Therefore, the extended Cox proportional hazards regression model and the generalized concept of relative risk are used to identify factors associated with the housing price. The analysis shows that the implementation dates of the macro regulation policies were related to the price changing trends. Qualities of public elementary and secondary schools were significantly associated with the housing price, and the associations between the structure and neighborhood characteristics and the housing price were influenced by the distance of the residential property to downtown Tianjin.",1.0,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042043
ef43ed7fc044986bf9a69476fc842879adb8f2eb,10.37407/kres.2023.41.1.141,The Impact of Regional Income and Housing Price on Fertility Rate,2023.0,Korea Real Estate Society,Ji Hyun Kim; S. Choi,"This study constructed a regional panel data set in order to investigate the impact of regional income and house price on fertility rate in 17 cities and provinces from 2013 to 2021. The fixed effects regression model is used to estimate the effect of intrinsic characteristics of individuals in the panel data set. According to previous studies, the increase in housing prices in the region negatively affects the fertility rate, but it is necessary to analyze it in connection with the income level of the region rather than simply considering housing prices. The results show that PIR has statistically significant negative impact on fertility rate. GRNI per capita also shows significant negative effect on marriage rate and fertility rate. It implied that regions with active economic activities and high house prices are more likely to have low rates of marriage and childbirth. Furthermore, the age at which a woman first gets married and private education expenditure have corresponded negatively to fertility rates. Our results suggest that each city and province should monitor the supply and demand of housing in order to prevent housing prices from soaring and substantially provide housi",1.0,
d2ad1eff2bb501f051295ba868648d26f7f771a7,10.1057/s41599-025-04463-1,The influence of administrative border on the spatial correlation of house prices: evidence from China,2025.0,Humanities and Social Sciences Communications,He Wang; Shujun Yang,"This paper empirically examines the spatial correlation differences and mechanisms of housing prices in prefecture-level cities, considering the influence of administrative boundaries. Using an extended spatial Durbin model and panel data from 292 prefecture-level cities in China between 2000 and 2013, the study identifies the attributes and stage characteristics of real estate. The results indicate a significant spatial correlation in housing prices between neighboring prefecture-level cities, with inter-provincial administrative boundaries reducing the spatial correlation between these cities by 62.11%. Further research reveals significant changes in the spatial correlation of housing prices following the 2007 subprime mortgage crisis, with the transformation of real estate’s dominant attributes being the primary reason for these changes.",1.0,https://doi.org/10.1057/s41599-025-04463-1
2e7190fdf8580f197e355fa037beceb0892847a7,10.54691/fq8w9z48,The Impact of Housing Price-to-Income Ratio on Fertility Rate,2025.0,Frontiers in Humanities and Social Sciences,Yan-Ke Song,"This study uses a regression analysis with a fixed model to explore the impact of housing price-to-income ratio on fertility rate as a whole, and also discusses the effect of related factors on fertility rate. This study uses panel data of 31 provinces (except Hong Kong, Macao and Taiwan) from 2017 to 2020 from the Statistical Yearbook of National Bureau of Statistics and Provincial Statistics Bureau. Research findings: (1) Housing price-to-income ratio has a significant negative effect on fertility rate; (2) Unemployment rate has no significant correlation with fertility intention; (3) Urbanization and educational attainment are negatively correlated with fertility intentions; (4) GDP per capita has a positive correlation with fertility; (5) Economic factors have become an important consideration in people’s fertility decisions.",1.0,https://fhssjournal.org/index.php/ojs/article/download/41/43
ecb8bacfa12ff5fad71503a1b459329f9f420bc4,10.52324/001c.66198,A House Price Modeling Based on Clustering and Kriging: The Medellín Case,2022.0,The Review of Regional Studies,H. Villada-Medina; J. F. Rendón-García; C. Ramírez-Dolores; G. Alcalá,"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.",1.0,https://rrs.scholasticahq.com/article/66198.pdf
3e7332fd9f58a586c3c9d445ed437ca462b753f4,10.70382/sjber.v9i4.044,"FLOOD RISK AND ITS INFLUENCE ON RESIDENTIAL PROPERTY VALUES: A CASE STUDY OF VICTORIA ISLAND, LAGOS STATE",2025.0,Journal of Biodiversity and Environmental Research,Akinsanya Alade Mohammed; Ibidapo Shonibare,"This study investigates the impact of flood risk on residential property values in Victoria Island, Lagos State, Nigeria. Victoria Island, a prime coastal area, has experienced recurrent flooding due to heavy rainfall, poor drainage systems, and rising sea levels. The research employs a mixed-methods approach, combining spatial analysis with surveys of property owners, estate surveyors, and residents. Findings indicate that properties in flood-prone zones suffer significant depreciation, with values declining by up to 10% compared to similar properties in safer areas. Factors such as accessibility, infrastructure, and neighborhood amenities also influence investment decisions. The study underscores the need for integrated flood risk management strategies to protect property investments and ensure sustainable urban development.",1.0,https://ssaapublications.com/index.php/sjber/article/download/785/1035
97a431e9949aba829ecf025df482b5f731c4c3ba,10.14704/web/v19i1/web19192,The Macroprudential Policy: An Analysis of Indonesia’s Financial System Stabilities,2022.0,Webology,Bakhtiar Efendi; Sirojuzilam; Irsad; Dede Ruslan,One of the macroprudential policies that are often used mainly in Asia and including Indonesia is the Loan-to-Value policy with a function to overcome systemic risks related to housing price spikes and credit distribution. Data analysis uses Vector Auto Regression (VAR) and simultaneous equations (Simultaneous Regression). The data in this study is time-series data. Exchange rate variables have negative impact on inflation because rate of exchange instability will affect capital flows or investment and international trade. And tax variables have an insignificant negative effect on inflation due to the impact of rising inflation due to the shock of tax increases in taxes that are seen as increasing production costs and sales costs to consumers but in contrast to credits that have an insignificant positive effect on inflation.,1.0,https://doi.org/10.14704/web/v19i1/web19192
539a3302ac7a56a96b5d8cd19129689a8490f814,10.1080/12265934.2024.2382710,A fine-grained investigation on the predictors of urban green space growth in Nanjing,2024.0,International Journal of Urban Sciences,Cong-Hui Zhou; Zhao Jin; Shining Zhang,"ABSTRACT Existing studies have primarily examined urban green space (UGS) evolution in isolation from the urban development context, neglecting the dynamicity of the factors affecting UGS growth in different spatiotemporal conditions and urban development levels. Taking Nanjing as the study site, we constructed spatial lag models using multi-source data and investigated the patterns and predictors of UGS growth across diverse spatiotemporal conditions in high-density environments. The model results show that: (1) although good natural conditions can support UGS development, this effect is greatly weakened in fast urban expansion as other land uses show higher priority in land occupation; (2) industrial and public service lands and commercial lands show a negative effect on UGS growth in the early and late urbanization, respectively, while residential lands demonstrated sustained negative effect in all the urbanization phases; (3) the effect of the total population and population structure varies in different urban development phases; (4) at an advanced urban development level, the areas close to city centre have better opportunities to install new UGSs. These findings offer useful ",1.0,
9d0dd482d11668944e21153d5e05cca723d2254f,10.1080/12265934.2022.2093260,The inception of housing pathways in urban China: the declining household formation of young adults from 2011 to 2017,2022.0,International Journal of Urban Sciences,Zhou Yu; Michael Haan; Cheng-Dong Yi,"ABSTRACT The homeownership rate of young adults has surged to an unprecedented level in urban China, despite rising housing prices and significant rural-urban migration. A trend analysis of nationally representative microdata shows that household formation is the missing link in the paradox and that many young adults aged 18–44 have failed to form independent households from 2011 to 2017, thereby delaying the start of their housing pathways. When factors such as socioeconomic and institutional attributes are controlled for, age differences in household formation decrease as expected. However, the age differences grow surprisingly larger over the study period, reflective of reform-induced changes in resource allocation. Further analysis demonstrates significant heterogeneity in headship status. While local young adults are squeezing into homeownership, migrants are overrepresented in the relatively stunted rental sector. Thus, while migration has brought newcomers to urban China and kept the headship rates from falling even further, institutional barriers have blocked migrants’ housing pathways. Overall, the pace of change is breathtaking. There is a growing divergence in young adul",1.0,
313dfd95a7e0ead61e66948f327799f756cfc245,10.1109/icimcis60089.2023.10349077,House Base-Price Prediction with Machine Learning Methods,2023.0,"2023 International Conference on Informatics, Multimedia, Cyber and Informations System (ICIMCIS)",David Gabriel Immanuel Simanungkalit; Belinda Meylia; Julius Salim; I. S. Edbert; Derwin Suhartono,"Various factors like location, size, age, and condition must be considered to predict house prices accurately. This study utilizes historical sales data and demographic information in conjunction with machine learning algorithms, such as Random Forest, XGBoost, Support Vector Regression, and Artificial Neural Networks, to forecast home prices. The Kaggle dataset ""House Sales in King County, USA"" was used to test the proposed models. This research aims to help buyers find affordable properties and make informed decisions. Previous studies have shown that machine learning algorithms are more effective than traditional regression models in predicting house values. By utilizing artificial intelligence and machine learning, this study aims to improve the performance and effectiveness of predicting home prices. Our findings indicate that the ANN model has the highest performance results compared to the other models, with an performance rate of 0.785 before tuning and 0.887 after tuning. XGBoost comes in second with an performance rate of 0.886 after tuning, followed by Random Forest with an performance rate 0.855 after tuning. Finally, SVR is in fourth place with an performance rate 0.84",1.0,
a1b7322c5d16c49a86342a6acdce41dc4882c32b,10.37134/jcit.vol13.1.4.2023,The Prediction of Affordable Housing Prices in Petaling Jaya District Using R Statistical Computing Environment,2023.0,Journal of Contemporary Issues and Thought,Suresh Nodeson; Kesavan Krishnan; Sathis Krishnan,"Affordable housing especially in a city environment is recognized as one of citizen needs among the mid-dle-income groups. This research paper intended to explore the possibilities to use data mining algorithms: Linear Regression, Random Forest and Gradient Boosting algorithms for predicting and analyzing the housing affordability price for middle-income earners in Petaling Jaya district, Malaysia. The dataset from Malaysia House Index by Petaling Jaya district used to evaluate based on the proposed algorithms. The dataset extracted based on residential property sub-sectors with three attributes. Based on the prediction models, as results, the Gradient Boosting algorithm shows higher accuracy of 74% for predicting affordable housing price in Petaling Jaya district, Malaysia compared to other prediction techniques.",1.0,https://ejournal.upsi.edu.my/index.php/JCIT/article/download/7579/4306/36719
508096b5c069147b5ed29acaa84d60b09d11e63e,10.1108/ijhma-06-2025-0142,Predictors of housing price cycle and housing bubbles in Malaysia: resident survey using multinomial logistic regression,2026.0,International Journal of Housing Markets and Analysis,A. Senadjki; Hui-Nee-Au Yong; Samuel Ogbeibu; Chee-Yin Yip; Mohammed Awal Iddrisu; Ahmad Nazri bin Wahidudin; Kok Hoong Woo,"
 
 This study aims to examine the predictors of housing price cycles and potential housing bubbles in Malaysia using resident survey data.
 
 
 
 The paper uses multinomial logistic regression to understand the factors influencing housing prices based on perceptions from building industry professionals, professionals outside the building industry, and non-professionals. The factors considered include housing demand, house buyers’ opinion of the current housing market scenario, present housing market conditions, government policies, foreign buyers, economic growth outlook, and demographic factors such as property acquisition period and intention to purchase property. A total of 500 questionnaires were distributed and collected from the respondents.
 
 
 
 The analysis reveals that housing demand plays a significant role in predicting housing bubble trends, with either a positive or negative influence depending on contextual factors. The findings indicate that market perceptions and macroeconomic conditions jointly shape housing cycle movements, offering empirical insights into housing market volatility in emerging economies.
 
 
 
 The paper deepens theoretical insights into the pr",1.0,
92718bc7b3b8f25d68058f82ee2b19a8000630f2,10.55041/isjem04918,House Price Prediction App,2025.0,International Scientific Journal of Engineering and Management,P. Abhishek; R. B. Sankar,"Predicting housing prices is a vital task in real estate and urban planning, influencing decisions by buyers, sellers, developers, and policymakers. This project presents a machine learning-based prediction system for estimating residential property prices using classification algorithms. Historical housing data across multiple cities in India was utilized, containing attributes such as area, number of bedrooms and bathrooms, year built, location, garage, and property condition. The system employs supervised machine learning models Random Forest, Gradient Boosting, and Linear Regression to forecast house prices. Among these, the Gradient Boosting model achieved the highest accuracy. The selected model is integrated into an interactive web application built using Streamlit, providing users with instant price predictions and model comparisons. Through intuitive visualizations and real-time predictions, this system aids individuals and professionals in making informed property decisions.

Index Terms: House Price Prediction, Machine Learning, Regression Models, Gradient Boosting, Random Forest, Linear Regression, Real Estate Analytics, Streamlit Application, Housing Data, Supervised L",1.0,
ca257557f3718964753fac26792bfb9d472b2d5c,10.1109/igarss52108.2023.10283134,Evaluation of the Impact of Urbanization Factors on Land Subsidence by GWR,2023.0,IEEE International Geoscience and Remote Sensing Symposium,Jiayi Zhang; Jian Gao; Shuohang Yang; Yunfei Guo,"Land subsidence has been observed in many cities with rapid urban expansion. Most studies have qualitative analyses of the influencing factors on land subsidence, such as the exploitation of groundwater and mineral resources. Few studies have quantitatively analyzed the spatial heterogeneity between land subsidence and urbanization. In this paper, the LiCSBAS (small baseline subset with the LiCSAR) method was employed to map the land subsidence rate in the central area of Chengdu from 2016 to 2018. Then geographically weighted regression (GWR) model was utilized to study the spatial variation relationship between ground subsidence and urbanization factors. The results show that the land subsidence rate in the study area ranges from -12.21 to 13.8 mm/yr., with a mean value of -0.09 mm/yr. The land subsidence is relatively stable, with no significant subsidence funnel. The GWR findings demonstrate that urbanization factors have different degrees of influence on land subsidence. Large trading areas and business facilities have the greatest influence, followed by residential areas and metro lines. This paper provides a novel and effective way to quantitatively study the spatial heterog",1.0,
9e03cca3c14bd64b56e858d50129d7cf526a6d64,10.1109/ic3i56241.2022.10072476,An Enhanced Regression Technique for House Price Prediction,2022.0,International Conferences on Contemporary Computing and Informatics,K. Lakshmi.; P. Narayana; P. Bhavani; V. V. S. Madhavacharyulu; N. Lavanya; S. Pousia,"The real estate market is one of the least transparent sectors of society as real estate prices change daily and are often overvalued rather than valued. Homebuyers use budget and market methods to find new homes. However, a fundamental problem with the current approach is the inability to predict future market trends that will lead to price spikes. It is very important for researchers to base their house price proposals on empirical studies. In order to accurately predict the price of a home, customers need to carefully evaluatefactors related to the home, which is very difficult. Using machine learning (ML) to solve this problem seems like a viable solution. To address this problem, ML models such as Linear Regression (LR), K Nearest Neighbors (KNN), Random Forests (RF); Ensembles (LR, KNN, RF) are used. A number of error metrics are used to select the best model, including mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The results in this disclosure show that a model combining linear regression (LR), random forest (RF) and K Nearest Neighbors (KNN) yields the lowest inaccuracies. A successful regression model should have a minimal error ",1.0,
a5050719ab7d00f255f95d1baa5589b39118508d,10.32609/0042-8736-2022-12-118-136,Real estate valuation based on big data,2022.0,Voprosy Ekonomiki,M. Mamedli; A. V. Umnov,"The paper considers the application of the web scrapping and machine learning algorithms for the assessment of the real estate price on the secondary housing market in Moscow. For this, we collect and process the data from the CIAN website and the data from “Reforma GKH”. To evaluate real estate objects, we consider such machine learning algorithms as Elastic Net, Random Forest and Gradient Boosting. We also apply Shapley vector-based approach to interpret the results of the black-box algorithms. The results suggest that the use of black-box algorithms in assessing the price of apartments on the Moscow secondary housing market allows to obtain more accurate price estimates both for different price segments and for the sample as a whole. At the same time, Gradient Boosting has demonstrated the best accuracy among other algorithms. Interpretation based on the Shapley vector shows that the total area, year of construction, ceiling height, renovation, as well as monolithic construction technology had a positive effect on the price. The price is negatively affected by the number of floors in the house, the possibility of mortgage and lack of repairs. Developed methodology can be applied",1.0,
cf7e5777ace1f775abfb4c43b163dda1352a6063,10.54691/bcpbm.v26i.1935,Empirical Evidence Based on Geographic Regression Discontinuity Analysis of Housing in Guangzhou School District,2022.0,BCP Business &amp; Management,Y. Ouyang; Keyue Sun,"In recent years, the ""school district housing boom"" has become a general concern as it disrupted the housing market, undermining educational equity and class mobility. In view of the real problem of housing premium in school districts, this paper selects the housing data of primary school districts in Guangzhou as a research sample, quantifies it and discusses the impact of education supply on it, so as to provide references and suggestions for subsequent policy introduction. The main research conclusions of this paper are as follows: There is a premium for housing in Guangzhou’s school districts. After excluding the influence of location and physical factors, it is calculated that the housing price from Longdong School District to Yongping School District has increased by about 42.3%.",1.0,http://bcpublication.org/index.php/BM/article/download/1935/1933
a76c77fe27a907a79c6983a43c91d3ba04b45a76,10.2991/aebmr.k.220307.019,The Research of House Purchase Restrictions on Real Estate,2022.0,Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022),Min Li; Lu Wang; Jiayi Xu,"As my country's leading consumer market, real estate occupies a vast market consumption share. This paper uses multiple linear regression to rationalize the data to study the impact of housing purchase restrictions on Shanghai’s housing prices, housing demand, consumer purchasing power ,and social welfare. The research results show that since the purchase restriction order, the affordability of Shanghainese has been reduced, the supply of real estate in Shanghai is in short store, which aggravates housing prices, and harms the society due to false divorce. It is hoped that the research in this article will enable people to have a more deep understanding of the housing purchase restriction policy to develop the real estate industry in Shanghai better and carry out effective regulation.",1.0,https://www.atlantis-press.com/article/125971607.pdf
9ca358b99e4f5465a0aeaa938741b17ddb56dbd2,10.1145/3795546.3795587,Predicting Real Estate Prices: A Gradient Boosting Tree Approach,2025.0,Proceedings of the 2025 International Conference on Digital Transformation and Management,Yanjie Jiang; Linlin Yue; Shuting Yang; B. Yan,"This study applies a Gradient Boosting Tree (GBT) model to predict housing prices in a Chinese secondary city, utilizing comprehensive real estate transaction data. The optimized model identifies historical community transaction volume (19.8% contribution), total floors, and unit area as key pricing determinants. Under rigorous temporal validation, it achieves a robust RMSE of 1,611 CNY/m² and R² of 0.54, reflecting true forecasting capability without data leakage. The analysis provides multi-stakeholder insights: developers should prioritize location-based valuations; homebuyers require model-driven tools for price assessments; policymakers need dynamic monitoring systems to regulate market volatility. Methodological innovations include integrating geospatial data and consumer preference metrics, enhancing model applicability across diverse urban contexts. While the GBT framework shows promise for housing market analysis, challenges persist in model interpretability and cross-regional adaptation. Future directions emphasize hybrid AI approaches combining tree-based models with explainable neural networks, alongside expanded data fusion strategies. This research establishes a scala",1.0,
24c448fab0501539d4dac7ac6d712dca829981b4,10.37727/jkdas.2024.26.1.211,An Analysis on the Dynamic Changes of Korean Housing Prices,2024.0,The Korean Data Analysis Society,Dong-Woo Ko; Seong-Min Yoon,"Housing is one of the essential goods that determine the conditions of life. However, housing prices fluctuate widely depending on macroeconomic conditions, making it an important research topic. This study analyzes the dynamic changes in quarterly housing prices from 2006 to 2023, using GARCH model, Granger causality test, and vector error correction model(VECM). The main finding are as follows. First, there are inertial feedback effects in house price changes. Second, there is clustering and long-run persistence in the dynamics of house price volatility. Third, we find significant effects of housing loans on the demand side and housing construction permits and unsold housing units on the supply side on house prices. Housing loans have a positive effect in the short run, while housing supply variables such as housing construction permits and the number of unsold housing units have a negative effect in the long run. These results of our analysis suggest that the housing market follows the market principle that the role of housing supply and demand works well, and that housing prices have inertial characteristics with spikes and crashes depending on the economic environment. Housing",1.0,
9ac1b308913eb222115dad5102d1517d18245d72,10.2139/ssrn.2272583,The Impact of the Crime Rate and Star Schools on House Prices: An Analysis of Spatial Dependence,2013.0,,Chun-Chang Lee; Wei-Lun Chang; Yun-Yan Wu,"This paper discusses the relationships among house prices and house characteristics, crime rates, and star schools. The analyzed data were collected from 12 administrative regions in Taipei city. The empirical results show that star schools have a significant positive impact on house prices; violent crime rates have a negative but not significant impact on house prices; and theft-type crime rates have a significant positive impact, contrary to expectations. We infer that the cause for this counter-intuitive finding may be the psychological burden, but may also be related to theft-type crime rates in commercial development areas. The results also reveal that using the spatial error model and the spatial lag model will modify overestimated parameters in the conventional OLS model.",1.0,https://doi.org/10.2139/ssrn.2272583
3493dbf1b68b2391cd34bbe6db2325637f0d11eb,10.11361/reportscpij.14.3_196,Neighborhood Type Classification by the Associations between Residential Burglary and Physical / Social Environment,2015.0,Reports of the City Planning Institute of Japan,Masaya Uesugi; Kimihiro Hino,"This study examines geographical areas with similar associations between the crime rate of residential burglary and a range of physical and social environment such as the percentage of detached and low-rise apartment housing, the distance to the city center, the ratio of commercial-to-residential area, the average household size, population aging rate and high-income household rate in Tokyo neighborhoods. A geographically weighted regression shows spatial heterogeneity of these relationships and a cluster analysis based on these parameter estimates objectively classifies neighborhoods into several zones with relatively homogeneous parameter values. These findings suggest that different crime prevention approach should be taken according to the place-based crime risk structure.",1.0,https://www.jstage.jst.go.jp/article/reportscpij/14/3/14_196/_pdf
d28c93f15091e564b3897e561c0652d8c5ea7635,10.1109/aic61668.2024.10730866,Extensive Data Preprocessing and Exploratory Data Analysis for House Price Prediction using Machine Learning and Deep Learning with an Introduction to AutoML Technologies,2024.0,International Workshop on Artificial Intelligence and Cognition,A. Ved; Archana Gupta,"Houses contribute to one of the most essential needs of every human and house prices play a very important role in everyone’s life. House Price prediction has been one of the crucial topics in the real estate industry. This paper focuses on data preprocessing techniques which are carried out using dataset containing the house prices of the Ames City in United States. It covers the entire journey right from Data collection to Data Preprocessing and Data Analysis to Prediction using various Models. In this paper, Machine learning, Deep Learning and Auto ML techniques are applied to study historical property transactions in Ames to find useful models for both house buyers and sellers. The Models we test as a part of this paper are Random Forest Regression, Ridge Regression, Lasso Regression, (SVR) Support Vector Regression, XGBoost (which is an optimized distributed gradient boosting library) and ANN (Artificial Neural Networks). While ANN and Automated Machine Learning (AutoML) represent the forefront of machine learning, our exploration demonstrates that classical ML models remain indispensable across various domains. Our findings also demonstrate that the effectiveness of any machi",1.0,
5408d8a8b1e9e29b6b38c0d002423e0c45e012a0,10.1111/pbaf.12373,Effects of property value assessment limit portability on migration outflows,2024.0,Public Budgeting &amp; Finance,Stuart P. DiDonato; Antonios M. Koumpias,"This study examines the impact of the 2008 portability provision of Florida's property value assessment limit, which permits homeowners to transfer the benefit of assessment limitations, on in‐state out‐migration. Leveraging IRS county‐to‐county migration data and quasi‐experimental designs, we find increased household and individual in‐state, county‐to‐county outflows following portability implementation in Florida relative to other states by 478 households (4.8%) and 675 individuals (3.5%). Our results suggest that natural amenities, rather than home‐ownership rates or property value growth, drive migration outflows. Robustness checks reveal heterogeneous responses across metro and nonmetro counties, suggesting that urbanization may accelerate after adopting the provision.",1.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/pbaf.12373
39d5c22f1be5baaf32decd226fe97eb269dc665c,10.1080/10511482.2024.2376071,Can Property Taxation Fix China’s Housing Market?: A Regression Discontinuity Analysis,2024.0,Housing Policy Debate,Xin Li; Kyung-Min Nam; Zheng Chang,"Abstract How does the housing market respond to a newly imposed value-based property tax? Based on individual property transaction data spanning seven years, we investigate the repercussions of a new property tax on the local housing market in Chongqing, China. In particular, we adopt a fuzzy regression discontinuity approach to examine the impact of the tax policy on house prices, transaction volumes, and the broader social welfare. We find that the property tax in Chongqing has not caused a reduction in house prices, but it has made a non-trivial contribution to local public finance for public housing programs. Our findings suggest that the property-tax-capitalization hypothesis is not supported in Chongqing’s context, although the tax improves wealth redistribution.",1.0,https://hub.hku.hk/handle/10722/344684
2d751fc0b22eccb1619e42885503c4b375305346,10.26417/ejme.v3i1.p19-30,VECM Analysis to House Price Index. Case of Tirana,2020.0,European Journal of Marketing and Economics,E. Marku; Llesh Lleshaj; Arjana Lleshaj,"This paper analyzes long-run equilibrium of “house price index” in Tirana (the capital city of Albania) achieved by the long-run performance of macroeconomic factors.We have used the techniques and analysis of linear multiple regression by VECM (vector error correction model), to identify endogenous factors, that effect the stability of “house price index”. The analyze is based on data series 2010-2018 (with 3-month frequency), with independent variables: mortgage loan, interest rate on long-term loans, construction cost index, EUR/ALL exchange rate, house price index with lag(1).We conclude that all these independent variable (expect EUR/ALL exchange rate) are statistically significant, in long-run equilibrium and in the elasticity assessment of “house price index”.",1.0,https://doi.org/10.26417/ejme.v3i1.p19-30
730ff7c122de12ce15062db20663021c26be889b,10.13128/aestimum-12342,Simulating housing prices in Lyon with UrbanSim: predictive capacity and sensitivity analysis,2013.0,Aestimum,Marko Kryvobokov; Aurelien Mercier; A. Bonnafous; D. Bouf,"Housing prices in the Lyon Urban Area are simulated with the land use framework UrbanSim interacting with transportation model. We focus on the Real Estate Price Model of the UrbanSim framework. This OLS regression model of housing prices is calibrated using a nine-year back-casting period. The calibrated model, applied in simulation, provides price dynamics similar to actual one in the centre of Lyon. Sensitivity analysis demonstrates the model’s ability to capture changes in employment accessibility on price dynamics. We conclude that the calibrated residential Real Estate Price Model from the UrbanSim application in Lyon is sensitive to changes in accessibility and provides good predictive capacity in the city centre, but underestimates prices in other areas.",1.0,https://oaj.fupress.net/index.php/ceset/article/download/5599/5599
7f50f7507d0816864e029da4808c78f120116ba6,10.1109/icsintesa62455.2024.10747910,Regression Modeling for House Price Prediction in Java Island,2024.0,2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA),Faqih Hamami; I. A. Dahlan,"Houses have become significant commodity for many parties. For developers and buyers, houses prices are crucial considerations for property investment. Typically, house price predictions are made by experts. Automating predictions would ease various parties, including the government, in controlling price stability. House price predictions are extensively researched in various countries Like in China, Australia, Taiwan, Pakistan, India, Canada, Indonesia, and others. Generally, features for price prediction revolve around the size and furniture of the house without considering its location. House price in larger cities tend to be more expensive. Given this situation, this research focuses on the impact of city and province attribute on house price prediction. This research proposes to build a predictive model based on home size, furniture, and location. The model is built using several machine learning algorithms for regression measured by Mean Squared Error (MSE) metrics. Testing results show that predictions tend to improve when using location attributes for almost all algorithms. Additionally, simulations are conducted using other feature engineering and categorical encoding, inc",1.0,
74468b5133889de393760ea45645b2dce9a80818,10.47392/irjash.2023.s017,A Literature Review on Using Machine Learning Algorithm to Predict House Prices,2023.0,International Research Journal on Advanced Science Hub,Tanmoy Dhar; M. P,"In this study, we use a variety of machine-learning methods to forecast the sale prices of residences. The size, location, building type, age, number of bedrooms, garages, and other characteristics of the property all affect how much it is worth when it is sold. Machine-learning algorithms are employed to develop the prediction model for houses in this article. Using machine learning methods, such as call trees, supply regression, support vector regression, and the Lasso Regression methodology, a prognostic model is developed in this case. Also, we have contrasted supported parameters for these algorithms such as MAE, MSE, RMSE, and accuracy. In this research, machine learning algorithms are used as a hunting tool to create models for predicting housing value.",1.0,https://doi.org/10.47392/irjash.2023.s017
45215252ead8c85d5b03572956aa87130ffe6a7e,10.4028/www.scientific.net/amm.638-640.2436,Study on Commodity Residential House Price Volatility of the Guanzhong Urban Agglomeration Using Spatial Econometric Analysis,2014.0,,F. Lan; Ying Tian,"This paper was based on the theory of spatial econometric model. It selected the panel datas of the guanzhong urban agglomeration of five core cities from 1998 to 2012, and inspected the commodity residential house price if there is a space dependency relationship between the two cities. On the basis to analyze the main factors influencing the commodity residential house price volatility and research on the influence of the housing price direction. Results show that the sample is significant spatial correlation between urban housing prices. Xi 'an have great influence on regional cities housing price. The urban population, household disposable income, land acquisition costs, sales area are the main influence factors affecting housing price volatility.",1.0,https://doi.org/10.4028/www.scientific.net/amm.638-640.2436
5c44228eab8defddfb00d8e4526f3123e75db18a,10.30534/ijeter/2020/70842020,House Sale Price Prediction using Advanced Regression Techniques and AutoML (TPOTRegressor),2020.0,International Journal of Emerging Trends in Engineering Research,Tejaswi V. S. B.,"The increasing and decreasing occurrence of house prices changes from time to time. There are more reasons that effect the fluctuation of house prices. Some are build year, location, physical amenities, size of house etc. Predicting the value of house (Sale price) helps the customers to take right choice of buying the house. Machine learning is being adapted for various fields that could build prediction model and estimate the outcomes. In this paper, we are contemplating the issue of rise and fall of house rates as a regression problem. Regression is a process that aims to predict the correlation between target dependent feature and a sequence of other changing independent features. In our experimental analysis, we are using Decision Tree Regression",1.0,https://doi.org/10.30534/ijeter/2020/70842020
6ec286075cc79f13d39cd02738557bcd5cb08a3f,10.1007/s12205-019-1894-0,The Correlates between Walkable Environments and Housing Price using Multi-level Model,2019.0,KSCE Journal of Civil Engineering,Yeol Choi; M. Seo; S. Oh,"We examine 554 state basic districts and 1,832 householders in Busan metropolitan city to see whether the physical and walkable characteristics of housing influence apartment prices. We use two-level hierarchical linear models with multilevel modeling for a more accurate analysis. Walkable characteristics in level 2 state basic districts potentially explain 77% of the variation in housing values. Overall, our hierarchical spatial analysis based on new state basic districts in Korea explained price variation better than previous studies, which considered each householder. The results provide policy opportunities for planners and citizen groups to pursue strategies that encourage the development of walkable and sustainable neighborhoods.",1.0,https://doi.org/10.1007/s12205-019-1894-0
6aac8be4886f5ad5c962ffa19defe11abc151a81,10.47260/bae/826,Macroeconomic and financial determinants of non-performing loans: Evidence from PIIGS,2021.0,Bulletin of Applied Economics,Panagiotis Magdalinos; Ioannis Tsakalos,"The Eurozone Debt Crisis led to the outbreak of non-performing loans (NPLs). The purpose of this paper is to identify the macroeconomic and financial factors that enhanced the non-performing loans. We compare the impact of financial crisis to the following countries: Italy, Greece, Spain, Portugal, Ireland (PIIGS) and focus on specific parameters which affect the banking sector. We apply panel data analysis to highlight the relation among NPLs and specific parameters: Gross Domestic Product (GDP), unemployment rate, bank liquidity, and the real estate market. Findings confirm the negative correlation between NPLs and the GDP and the housing prices, as well as the positive one with the unemployment rate and the liquidity ratio (LDR).",1.0,https://www.riskmarket.co.uk/bae/journals-articles/issues/macroeconomic-and-financial-determinants-of-non-performing-loans-evidence-from-piigs/?download=attachment.pdf
97f31e10003707306832d397074386a94f12ef39,10.1177/10575677261428020,The Relationship Between Crime and Property Values at the Micro Geographic Level: Examining Diverging Developmental Trends,2026.0,International Criminal Justice Review,Sabo-Brants Hagit; W. David; Amram Shai; Shay Maor,"This study examines the relationship between crime trajectories and housing prices at the micro-geographic level, focusing on street segments in Tel-Aviv-Yafo. It combines two analytic strategies: a quasi-experimental comparison of matched segments using group-based trajectory modeling, and multivariate ordinary least squares regressions predicting logged housing prices. In the quasi-experimental analysis, matched pairs of street segments from a typology of nine crime trajectory groups began with similar crime levels but diverged over time. Segments that experienced steeper increases in crime tended to show slower housing price appreciation. In the multivariate models, a 10-crime increase between 2005 and 2014 was associated with approximately 3–4% decline in price per square meter (2013–2016). These effects remained robust after adjusting for demographic, land-use, and built environment characteristics, and controlling for spatial dependence. The findings advance the criminology of place by showing that crime is a dynamic and spatially concentrated force with measurable economic consequences. From a policy perspective, targeted crime prevention in high-trajectory areas may enhance",1.0,
bf84a226d199471074feb325dabe786163df315b,10.68479/001c.8515,The Use of Alternative Measures of Accessibility in Estimating Spatial Relationships,1978.0,Journal of Regional Analysis &amp; Policy,G. McDougall,"Examination of the literature discussing the effects of accessibility on the spatial distribution of economic phenomenon (in particular property values and rents, and population densities) reveals that there is no clear consensus among investigators as to what observed variable adequately or appropriately measures the concept of accessibility or spatial proximity. This paper summarizes the results from some simple comparisons when alternative measures of accessibility are used to statistically describe characteristics of house value, rent, and density gradients. It is argued that high correlations among alternative operational measures of accessibility are not sufficient to justify their interchange in commonly applied estimation",1.0,http://ageconsearch.umn.edu/record/128324
768894e40c1ad4c0be4821db372ae199252fed64,10.54254/2755-2721/6/20230928,Optimization of linear regression in house price prediction,2023.0,Applied and Computational Engineering,Liang Zhu,"House price prediction plays a very important role in housing transactions. Linear regression based algorithms show good effects in predicting house prices. They have strong interpretability and fast operation speed. However, people ignore the estimation of deviations in linear regression (LR) algorithms. In this paper, k-nearest neighbor (KNN) algorithm is supposed to estimate deviations that are added to the result of linear regression to predict house prices accurately. Furthermore, deviation regression (DR) algorithm is supposed to make the prediction result more accurate. By utilizing Boston House Price data from Kaggle, extensive experiments are conducted and demonstrate the superior performance and compatibility of DR.",1.0,https://www.ewadirect.com/proceedings/ace/article/view/2126/pdf
86f2ddd012cb723848c2dd3c201a6fc8a80b1058,10.24957/hsr.2019.27.2.33,Determinants of Housing Mortgage Prepayment in Korea: Using a Loan-Level Dataset,2019.0,Korean Association for Housing Policy Studies,So-Young Lee; Seungkyu Park; Chang-Moo Lee,"This study empirically investigates housing mortgage prepayment behavior decisions in Korea. Using the loan-level data from 2013 to 2014, a Cox proportional hazard model and a shared frailty model which controls the effect of unobserved heterogeneity among local markets are estimated. The results show that there exists significant heterogeneity among the local markets. Ignoring this heterogeneity results in errors in estimating the prepayment behavior of borrowers. Estimation results show that the higher expected housing price change, the lower the hazard rate of prepayment and the higher collateral value change, the higher the hazard rate of prepayment. The sale for capital gains as well as refinance by option value may also cause prepayment. The greater the degree where the collateral value declines, the higher the hazard rate of prepayment. It means that distressed prepayment is an alternative when the collateral value declines. Estimation results also indicate that loan characteristics like LTV, loan amount, maturity period, grace period, and borrower characteristics like borrower’s age, income, credit rating are determinants of prepayment. Especially, low credit serves as a co",1.0,
63b8d283c7979bfd3be047e085b03a7f5e27f190,10.1108/pm-12-2021-0106,Designing an optimal neural network architecture: an application to property valuation,2022.0,Property Management,Changro Lee,"PurposeThe success of a neural network depends on, among others, an architecture that is appropriate for the task at hand. This study attempts to identify an optimal architecture of a neural network in the context of property valuation, and aims to test the ability of connecting related neural networks to reduce the property valuation error.Design/methodology/approachThis study explores efficient network architectures to estimate land and house prices in Seoul, South Korea. The input is structured data, and the embedding technique is used to process high-cardinality categorical variables.FindingsThe shared architecture of a network for simultaneous estimation of both land and houses was revealed to be the best performing network. Through weight sharing between relevant layers in networks, the root-mean-square error (RMSE) for land price estimation was reduced significantly, from 0.55–0.68 using the baseline architecture, to 0.44–0.47 using the shared architecture.Originality/valueThe study results are expected to encourage active investigation of efficient architectures by using domain knowledge, and to promote interest in using structured data, which is still the dominant type in ",1.0,
af9fbd307eed2741803c50fb9a7030de55caecc1,10.4028/www.scientific.net/amr.1079-1080.1093,Study on the Influencing Factors of the Western Provincial Capital Cities’ Housing Price Differences,2014.0,,Xi-Lu Liu; Jian-Ping Yang,"In recent years, the regional difference of the real estate price in western area gradually increases, making the effects of macroeconomic regulation and control is different among regions. In this paper, based on the theory of market supply and demand, first of all, the indicators are selected that can reflect the conditions of real estate market, then western provincial capital cities’ real estate markets are divided into three regions through cluster analysis. Secondly, the natural logarithmic model is established concerning the commercial housing price and the factors. Multiple regression analysis is performed for each area, in order to analyze the reasons of property price differences in three regions.",1.0,https://doi.org/10.4028/www.scientific.net/amr.1079-1080.1093
f2ccd05869601ee7ac8f142e70e6ac429b9431cc,10.1117/12.2655963,Multi-feature house price trend prediction based on machine learning,2023.0,Conference on Intelligent Computing and Human-Computer Interaction,Long-Yang Geng; Qihang Ran; Gengyuan Zhang; De-Hua Zhou,"As a pillar industry in China, real estate has provided great help for economic development. At the same time, buying a house has become a topic everyone cannot avoid. Until now, many scientific literatures and materials have proved that gradient boosting is a basic strategy. In this paper, the authors first clean the captured data and select features. Then, three methods eXtreme Gradient Boosting (XGBoost), Random Forest, and Bi-directional Long Short-Term Memory (BiLSTM), are used to predict housing prices in Beijing, one of the most representative cities in China, to investigate and compare the efficiency gradient methods of the three methods. The above three models process and predict 23 factors that affect housing prices by collecting information on houses sold and sold in Beijing in recent years. The score curve is obtained by fitting, finally, the best model of prediction is selected by adjusting the parameters. The influence of different characteristics on housing price prediction is studied. The experimental results show that compared with XGBoost and random forest, BiLSTM has a greater advantage in forecast speed and accuracy and is the most suitable model for predicting ",1.0,
617738a89ff272112e6f8804b981aa1981f5a232,10.20955/wp.2014.050,Semi-Parametric Interpolations of Residential Location Values: Using Housing Price Data to Generate Balanced Panels,2014.0,,Jeffrey P. Cohen; Cletus C. Coughlin; J. Clapp,"We estimate location values for single family houses by local polynomial regressions (LPR), a semi-parametric procedure, using a standard housing price and characteristics dataset. As a logical extension of the LPR method, we interpolate land values for every property in every year and validate the accuracy of the interpolated estimates with an out-of-sample forecasting approach using Denver sales during 2003 through 2010. We also compare the LPR and OLS models out-of-sample and determine that the LPR model is more efficient at predicting location values. In a balanced panel application, we use GMM estimation to examine how the location value estimates are affected by airport infrastructure investments.",1.0,https://s3.amazonaws.com/real.stlouisfed.org/wp/2014/2014-050.pdf
7567f31bec28fb34f0e2b7adb02c5cb497803344,10.1109/ocit65031.2024.00075,Real Estate Price Prediction: Optimized Ridge and Lasso Regression Analysis,2024.0,2024 OITS International Conference on Information Technology (OCIT),Kausik Challapalli; Prashanthi Thota; Harsha Garikapati; Veera Manikanta Sai Adusumilli; Satish Anamalamudi; ReddyPriya Madupuri,"Our second application, developing a reliable house price prediction model, kept housing as a core part of the data world. Data munging including handling of missing values, outliers and categorical variables is a first step of this process. Using these steps, data is explored intuitively, new features like price per square meter are created and further visualization and statistical analysis is conducted. For pattern selection, we use Ridge and Lasso regression, with GridSearchCV being run to tune and optimize parameters. We assess model performance, model assumptions by examining residuals using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R squared metrics. Finally built, the model can be used by real estate professionals, investors and policymakers to accurately predict housing prices based on factors such as location, area type, square footage, total number of buckets and offers a highly valuable statistical analysis of these values. A number of future work directions could include further expanding the model by adding new variables, using more sophisticated algorithms, or carrying out similar analyses in other regions of the developing world to achieve higher ",1.0,
e4cdee30928282bdc1086712cb40640d9a0250af,10.1177/0739456x231226100,Transit-Oriented Development and Neighborhood Change: The Case of Gent Sint-Pieters Station,2024.0,Journal of planning education and research,Jente Versigghel; Frank Witlox,"The Gent Sint-Pieters (GSP) project, an infrastructure and urban renewal project in Ghent, Belgium, can be considered a typical example of transit-oriented development (TOD). TOD can have significant effects on surrounding neighborhoods and their characteristics. In this study, a difference-in-difference model was used to measure changes in income level, educational attainment and house prices resulting from the GSP project. Although research demonstrates that TOD can lead to changes in the composition of neighborhoods and property values, our results show no significant causal effects, indicating that the local context determines whether, to what extent and in what way neighborhood changes occur.",1.0,https://biblio.ugent.be/publication/01HY3H7KK0H7Y0DZVT45492KNY/file/01HY3HJ71Z54GNZ6STDXQCE1MN.pdf
cfd025983e48b065823ded15da792750d8e54d71,10.55041/ijsrem54127,Machine Learning Based House Price Forecasting,2025.0,International Journal of Scientific Research in Engineering and Management,P. Kumari; Bharti Verma; Princy Kumari; Abhishek Sharma,"Abstract
House price prediction is one of the most important applications of machine learning in the realestate sector. With increasing urbanization and rapid growth of housing markets, accurate prediction of property prices has become crucial for buyers, sellers, investors, and financial institutions. This research focuses on developing a machine learning model using Linear Regression, Decision Tree, and Random Forest algorithms to estimate housing prices based on features such as location, area, number of bedrooms, bathrooms, and other structural parameters.

The system integrates a web-based front-end with a Python Flask backend and utilizes a trained machine learning model serialized using a pickle file. The proposed model aims to improve decision-making, reduce valuation errors, and automate price estimation. Results show that the system achieves more than 95% prediction accuracy, demonstrating the effectiveness of the model. This paper also discusses the applications, challenges, and future scope of the system.

Keywords:
Machine Learning, House Price Prediction, Random Forest, Linear Regression, Decision Tree, Data Preprocessing, Flask Web Application, Real Estate Analytics.",1.0,
67341a92a39c9538053d502402f1346d0856e24f,10.1109/iit59782.2023.10366490,Quantification of Explainability in Black Box Models using Complexity Measures,2023.0,International Conference on Innovations in Information Technology,Ngozi Nneke; Huw Lloyd; Keeley A. Crockett,"As a result in the rapid growth of explainability methods, there is a significant interest, driven by industry to develop methods for quantitative evaluation of such explanations. The availability of standard explainability evaluation methods would result in the ability to develop models that suit different stakeholders in different use cases. To address this issue, we propose three measures of the complexity of explanations based on Linear correlation, Monotonicity and $\phi_{K}$. We evaluate these measures on three tabular datasets (Ames House Price, Auto Price, and Wind). We investigate how these complexity measures vary with model accuracy. Our results show that model accuracy varies with complexity measures across the datasets. These variations indicate that models can be developed with the same accuracy but with of models less complex explanations as a result of varying the hyperparameters. We observe a trade-off between complexity measures and model accuracy which is evidenced in Pareto-fronts. We suggest that our metrics could be used for the development of multi-objective optimisation methods for machine learning models with tunable accuracy and simplicity of explanation.",1.0,
26fb15986f0480b2d6254e988108f90b486b4b3b,10.36871/2618-9976.2025.08.010,ECONOMETRIC MODELING OF FACTORS INFLUENCING REGIONAL HOUSING PRICES,2025.0,SOFT MEASUREMENTS AND COMPUTING,S. V. Kurovsky; D. Mishin; Bogdan M. Amelin,"This article is devoted to econometric modeling of factors influencing regional housing prices. In the national economy, the development of the residential real estate market is of significant relevance, since residential real estate objects play the role of the main investment asset for the real sector of the Russian economy. At the same time, the analysis of regional housing prices allows us to determine the current state of the residential real estate market, the determinants affecting it, and emerging trends. The results of such analysis become the basis for recommendations for developers and investors in the context of legal and economic reforms in the Russian Federation. At the same time, a comprehensive study of the domestic residential real estate market enables various stakeholders to make informed management decisions on the acquisition, construction of housing, or financial investments in it. Practical application of the panel regression model of pricing in the domestic residential real estate market contributes to a deeper understanding of the impact of existing determinants on housing prices, which increases the accuracy of further forecasts of the market in question.",1.0,
54d9981a76902db55ddf8b659b0acdc34fb36f94,10.1177/00420980261468191,Housing wealth accumulation as a driver of inequality: Uncovering housing appreciation dynamics across urban China,2026.0,Urban studies,Can Cui; Yunjia Yang; Jin-Qiao Long; R. Arundel; Richard Ronald,"
 Urban households in China have accumulated substantial housing wealth over recent decades, driven by rapid growth in homeownership rates and housing values. However, the ways in which housing wealth has evolved under changing market conditions remain insufficiently understood. There is also limited research on how housing price appreciation is distributed across households and how it contributes to disparities in wealth accumulation. This study addresses this gap using data from the China Household Finance Survey. Employing a GSEM, we examine households with varied socioeconomic characteristics – including
 hukou
 status, education, occupation and income – entering the housing market at different times and via divergent channels, to identify the factors influencing housing appreciation rates. The findings reveal considerable variation among social groups and across city tiers, highlighting the significance of timing and geographical disparities. The results suggest that the dynamics of housing as an investment asset are reinforcing existing inequalities, underscoring the need for policies that promote access while addressing wealth inequality and market distortions.
",1.0,
e9880dcaa7d82c10e1cb04ea0ed0261e161aa2d0,10.2139/ssrn.3215091,"How Large are Road Traffic Externalities in the City? The Highway Tunneling in Maastricht, the Netherlands",2018.0,Social Science Research Network,J. Tijm; T. Michielsen; R. van Maarseveen; P. Zwaneveld,"Infrastructure projects are increasingly aiming to improve liveability, in particular in urban areas. We analyse a specifi c case in which an existing highway in an urban area was moved underground in order to improve intercity traffic flows and to reduce traffic externalities. As travel times within the city hardly changed, this allows for a clean identifi cation of the value of traffic externalities. We find that the liveability bene fits of such integrated infrastructure are substantial relative to the construction costs. Each halving of distance to the tunneled segment is associated with 3.5% more appreciation in house prices since the start of the project.",1.0,https://doi.org/10.2139/ssrn.3215091
ffd8090aa7d0a4b6fc0ffd54b35386ae3a7f2c27,10.1080/00036846.2025.2504191,Place-based scholarships: an updated assessment of property value impacts from the Kalamazoo promise,2025.0,Applied Economics,W. J. Beasley; Jahangir Alam,"ABSTRACT Place-based scholarships are a policy to increase college enrolment rates and stimulate local economic development. A growing body of research indicates a positive correlation between such scholarships, academic performance, and out-migration. However, gaps in the research preclude a firm understanding of property value impacts associated with these scholarships. In this study, we use the quasi-experimental launch of the Kalamazoo Promise, a place-based scholarship available to students in Kalamazoo, Michigan, to estimate the treatment effect on single-family residential properties. Using property transactions between 2005 and 2025, we find property values increased by 12.4 to 15.0 percent for Promise zone homes relative to areas with comparable schools and home construction. The treatment effects identified in this study are slightly larger than previously reported estimates, suggesting that differences in amenities and school quality between adjacent regions may obfuscate market impacts. These differences are likely to permeate most analyses of promise zones, given their policy mission to stimulate growth and innovation in the region.",1.0,
d926fec602739e8bad051835b8a3e33a2eca5823,10.2139/ssrn.3519635,Attention and Housing Search: Evidence from Online Listings,2020.0,,Antonio Gargano; Marco Giacoletti; Elvis Jarnecic,"We study fluctuations in households’ attention to the housing market and their effects on home sales. Exploiting a unique dataset that tracks user activity on a major property website, we show that buyers’ attention positively responds to price growth in their postcode of residence. The increase in attention does not translate into higher effort allocated to inspecting individual listings, but in more extensive searches, covering a broader range of locations and property characteristics. These effects are mainly driven by homeowners, young households and residents of lower price postcodes in particular, consistent with higher price growth affecting users behavior through wealth effects or the easing of collateral constraints. Our results are stronger when postcode price growth is instrumented using a measure of local supply-elasticity. More extensive searches reduce segmentation on the demand side of the market, leading to higher prices and lower time on the market for homes listed for sale. This implies that fluctuations in households’ attention have procyclical effects on house price growth and generate spillovers within metropolitan areas.",1.0,
3f7f4aecfce6aa1081db207641ba6ac4e0fbdd4a,10.35136/krer.35.1.5,Determinants of Household Mortgage Loan Interest Rate Types,2025.0,Korea Real Estate Institute,Hyunjin Lim,"This study analyzes the factors influencing households’ choice between fixed- and variable-rate mortgage loans. Panel bivariate probit modeling was used to analyze data from the 2012–2023 Household Financial and Welfare Survey. Estimation results indicated that the probability of selecting a fixed-rate mortgage is low for households where the heads are in their 30s and 40s and with several employed household members and children. Conversely, households with large asset holdings, low debt levels, and low regular income are highly likely to opt for fixed-rate loans. Furthermore, a high debt–service ratio (DSR) is associated with a high probability of selecting a variable-rate mortgage. DSR represents the burden of principal and interest repayments on an existing debt. A country-level panel dataset was utilized to assess the impact of macroeconomic variables. Findings suggested that the share of fixed-rate mortgages tends to increase as the term spread narrows, GDP growth declines, consumer price inflation rises, and housing price growth slows. Additionally, high inflation volatility weakens the inflation-induced preference for fixed-rate loans.",1.0,
af624359e2e44f3855a502c7fd34dd0039180b6e,10.1109/otcon65728.2025.11070863,A Comparative Regression Analysis of House Price Prediction using Machine Learning Models,2025.0,2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0,S. Khattar; Akshra Ahuja; Vishesh Kansugia,"The ability to accurately predict the house price is fundamental within the real estate sector as it provides useful information to potential buyers, sellers, investors, and policymakers. This paper provides a complete comparative study on the machine learning models used in prediction of real estate prices by describing the linear models such as Linear Regression, Ridge and Lasso Regression models, as well as ensemble models like Random Forest, Gradient Boosting, XGBoost and Light GBM. The set of these models is evaluated on different datasets, specifically Indian real estate data by MAE, RMSE, RMSLE, R2 score, and even time for training. It is established in this study that ensemble models have been found to be more precise than the linear models, where XGBoost and LightGBM are the most productive, but need more time to execute. This study also highlights model and feature selection, and location information’s impact on productivity. It is factual that the above mentioned results are useful for all the stakeholders as they always try to adapt and make the best decisions in a fast changing and competitive real estate environment.",1.0,
f428e61245946863e0c4f89b563678fde786e9a2,10.5539/ijef.v9n1p194,The Determinants of Chinese Property Prices,2016.0,,Ming Qi,"In this paper, the determinants the housing prices boom in China are investigated. We extend the original model of Yuan and Xiao and Zhou (2006) by involving banks, in order to investigate the behavior of the money lender. A sample of 28 Chinese domestic commercial banks during the period between 2003 and 2009 are used for the empirical test. The results suggest that the mortgage loan has a significant impact on the property prices. Their effects on both condominium and residential prices are positive. However, the prices of High_Grade and Economical Houses have shown an entirely different response to the movement of mortgage credits.",1.0,https://www.ccsenet.org/journal/index.php/ijef/article/download/63635/35180
ed0a78a570106d2134318c26ae73122626b2145f,10.20527/jwm.v10i1.34,FACTORS AFFECTING PURCHASE DECISIONS OF HOUSES IN BATAM CITY,2022.0,JWM: Jurnal Wawasan Manajemen,Ronny Firdiansyah Arief,"This study intends to analysis the factors that influence consumers in the city of Batam in their decision to buy a house. The factors are attributes of the house, location, surrounding environment, and house prices. Samples were taken from 377 respondents who are home owners/occupants in the city of Batam and statistical analysis using SPSS version 24. Environmental factors have the most significant influence on home buying decisions, followed by house attributes and housing locations. While the price factor according to the results of data processing carried out, the data is not reliable so that its influence cannot be analyzed.",1.0,https://jwm.ulm.ac.id/id/index.php/jwm/article/download/34/185
6aa259d69df8435f746df77bb63443ce66398e25,10.1109/iccwamtip51612.2020.9317395,Analysis and Evaluation of Housing Price for Chengdu Urban,2020.0,2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP),Mu-Jie Li; Ming-Cang Zhu; Yong He; Peng-Shan Li; P. Zhang; Zhanyong He; Jibao Shi; Kai Chen; Tao Weng; Zezhong Zheng; Linghai Jiang,"House prices have received unusual attention in recent years, and evaluation of housing price has also become a research hotpot. Value of a house is considered as a mixture of various features, such as house type, orientation and location. Accurate assessment of houses thus faces a large number of challenges. Several methods have been applied to assess the value of a house, including geographically weighted regression and machine learning. However, every method has some limitations. The accuracy of traditional regression methods needs to be improved. Machine learning methods are weak in interpretability. In this paper, to analyze the housing price, we compared XBGoost, Random Forest, Support vector regression, back-propagation network and geographically weighted regression to model the second-hand housing price dataset, and analyzed the influenced of each feature. The result shows Random Forest reaches a lowest mean absolute error of 1517.29 and best r2 of 0.83. According to the constructed models, the importance values are given, and the area of house shows a strongest influence on housing price in the study area.",1.0,
cde72f5792891cdc73b2cf8d1c2497fb5ac0abc9,10.2991/aebmr.k.191225.051,Research on Threshold Effect of Housing Price on Real Estate Inventory,2019.0,"Proceedings of the 5th International Conference on Economics, Management, Law and Education (EMLE 2019)",Dong Zhang; Hao-Jie Wang,"Through the systematic GMM estimation of the panel data of 35 large and medium-sized cities in China from 2005 to 2016, this paper verifies the nonlinear relationship between housing prices and real estate inventory. On this basis, the threshold regression model is established by modifying the econometric model. It is suggested that cities with high real estate inventory should guide the housing price to return to a reasonable range, while cities with low inventory should increase land supply to strengthen housing security. Keywords—housing price; real estate inventory; system GMM estimation; threshold regression",1.0,https://www.atlantis-press.com/article/125931418.pdf
b2f53171261d88227971135a32f878e88c52de6a,10.48550/arxiv.2508.03156,Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions,2025.0,arXiv.org,Paul Gummer; Julian Rosenberger; Mathias Kraus; Patrick Zschech; Nico Hambauer,"House price valuation remains challenging due to localized market variations. Existing approaches often rely on black-box machine learning models, which lack interpretability, or simplistic methods like linear regression (LR), which fail to capture market heterogeneity. To address this, we propose a machine learning approach that applies two-stage clustering, first grouping properties based on minimal location-based features before incorporating additional features. Each cluster is then modeled using either LR or a generalized additive model (GAM), balancing predictive performance with interpretability. Constructing and evaluating our models on 43,309 German house property listings from 2023, we achieve a 36% improvement for the GAM and 58% for LR in mean absolute error compared to models without clustering. Additionally, graphical analyses unveil pattern shifts between clusters. These findings emphasize the importance of cluster-specific insights, enhancing interpretability and offering practical value for buyers, sellers, and real estate analysts seeking more reliable property valuations.",1.0,
7c113058e883735c4c3f2f82c128fbde6dede7ab,10.5120/ijca2022922043,House Price Prediction Review,2022.0,International Journal of Computer Applications,Smit Chatwani; Mayank Chotaliya; Vinaya Sawant,"The property market is among the most price-sensitive in the world, and it is constantly shifting. It is one of the most essential topics where machine learning techniques could be used to predict better and accurately. Physical conditions, concepts, and location are the three variables that impact the price of a home. The existing approach includes evaluating house values without considering market pricing or cost inflation. Future prices will be reduced by analyzing past market patterns and value ranges, as well as future improvements. This paper includes the study of various prediction models.",1.0,https://doi.org/10.5120/ijca2022922043
ffbb197247ac9a8dd59ae39b894d0327e0a28e2b,10.1109/icstsn61422.2024.10671079,A Comparative Study of House Price Prediction Using Machine learning and Deep learning Techniques,2024.0,2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN),Hamsavathi V; D. B; Subiksha R; Venkatesan R; Rekha D,"It is always a dream for everyone to own a house or flat., House prices increase every year, so there is a need for a system to, predict house prices in the future. House price prediction system will, help the developer to determine the selling price of a house and also, help the customer to make correct decision to look for a suitable, property to purchase. Most of the buyers expect the house based on, budget, prime location, property type, size etc. This paper presents, a comparative study on house price prediction using three distinct, models that is Artificial Neural Network (ANN), Naive Bayes, and, Random Forest (RF). Through comparative analysis, we evaluate the, performance of these models on a real-world housing dataset. The, outcome of this research contributes to advancing the field of real, estate prediction models that utilize the advantages of models. These, methods improved accuracy and efficiency in predicting house, prices, aiding house owners for reselling, real estate professionals, and investors facilitating informed decision in the ever-changing, real estate market.",1.0,
4c26c5f9aa96b69719a4056dd793d85a14f3d22d,10.1109/iccpct65132.2025.11176657,CNN-Based Image Segmentation Enhanced with K-Means Clustering: Insights from CIFAR-10 and YOLOv3,2025.0,"International Conference on Circuit, Power and Computing Technologies",Gopalam Rahul Malik; K. Jayasree; D. S. Murty; A. Vivek,"The contributions of Optuna in optimizing CNNs on CIFAR-10 increase model performance by a remarkable extent. It runs gradient boosting machines against linear models of regression to check whether they improve the accuracy of residential property price prediction by handling complex relationships. GBM proves very good at handling nonlinearities and predictor interactions, hence improving the real estate value prediction. It investigates advanced methodologies for improving neural network architectures, particularly on the problems of image segmentation and real-time clustering. This includes a description of CNNs with K-means and DBSCAN clustering, online K-means algorithms, semi-supervised learning, and spatial analysis. This work demonstrates improved accuracy across a range of datasets, including MNIST and real estate data. Performance metrics have been given for deep learning in image classification and object detection using CNNs on CIFAR-10. Object Detection with YOLOv3 in OpenCV and some notions of hyperparameter tuning using Optuna to increase the quality of a CNN.",1.0,
9ccca9972f19364d89b03ace388ea867d6dad158,10.5089/9781451927108.001.a001,The Determinants and Efficiency of Local Authority Spending in England,1991.0,Social Science Research Network,James P. F. Gordon; T. Bayoumi,"This paper analyzes the efficiency and determinants of local authority expenditure in England during 1989/90. The capitalization of local spending in house prices is used as a test of efficiency, and it is found that local services are in general excessive from this standpoint. The determinants of local government spending are then estimated and local incomes, the tax price faced by the electorate, and local party control are found to be significant. Application of the analysis to the recently introduced local government reforms suggest small effects on the overall level of spending.",1.0,https://www.elibrary.imf.org/downloadpdf/journals/001/1991/009/001.1991.issue-009-en.xml
3675c783b48fa906d2f3a7dc33dc44a756bafafc,10.4018/978-1-7998-3115-0.ch015,Housing Consumption,2020.0,,J. Marques; Paulo Batista; E. Castro; A. Bhattacharjee,"Housing is more than a living space; it represents a social status capable of promoting several forms and levels of socialization and segregation. Housing is characterized by a set of attributes and functions that are valued differently, in which the consumption of “good housing” is made considering the preferences of their attributes, perceived tangibly/intangibly by the consumers. The reciprocal relation between housing and space, and the lack of structured information capable of apprising the key drivers of such dimensions increase the complexity of understanding the rational mechanisms of evaluating the real housing value. Despite the challenges associated with the modelling of housing markets, urban studies and spatial econometric literature provide a broad (but unfinished) theoretical framework and practical tools that are able to describe, understand, and predict households' housing consumption. Thus, the aim of this chapter is to present concepts and techniques to rationally capture individual and collective housing preferences, and the way in which they interact.",1.0,
689b324dea3dfa29ca7fb470f439d401a80ff7d8,10.15863/tas.2017.09.53.10,"STUDY ON THE RELATIONSHIP AMONG MONEY SUPPLY, AND INFLATION ECONOMIC GROWTH: BASE ON THE STATISTIC DATA 1990-2015 IN CHINA",2017.0,,M. Du,"Relationship among money supply, and inflation economic growth is always a hot topic in academic world. In order to test the new phenomenon in them, Pearson correlation analysis and multiple regression analysis were used to analyze the internal relations among money supply, economic growth and inflation. Money supply and economic growth have high correlation ship, when money supply is high, the economy in China always in period of economic overheating. CPI cannot fully explain inflation in China inflation index should take house price and other elements in to consider.",1.0,http://www.t-science.org/arxivDOI/2017/09-53/PDF/09-53-10.pdf
2473b9ce03abe870ae7a62c32ec6bec16fe9da35,10.1080/14036096.2024.2326175,Exploring Cultural Determinants of Tenure Decisions: Evidence from an Owning-Centric Context,2024.0,"Housing, Theory and Society",Hasniyati Hamzah; M. N. Ismail; Rini Novelia Suawa,"ABSTRACT Most countries are still nations of homeowners, i.e. owning-centric despite home ownership being an economically irrational tenure due to its long-term financial risk. Seemingly, pre-existing social and institutional factors also shape people’s tenure decisions, which supports a cultural explanation of tenure decisions. This paper frames the examination of tenure decisions from a cultural perspective in Malaysia, a Southeast Asian country with a strong home ownership tradition characterised by inflated house prices in its major cities and high household debts. A qualitative approach was adopted, involving a focus group discussion (FGD) and an open-ended online questionnaire survey (OQS). Two input categories (parental and societal) and five cultural determinants (filial piety, parental care, emulation, virtue signalling and status symbol) of tenure decisions were discerned. Considering the current trajectory of the global economy and local housing market, an honest reconsideration of the home ownership culture in Malaysia is proposed.",1.0,
396076375d6cd79ed372ba61a61eb6720a867852,10.5539/ijef.v12n4p11,A Changing Weights Spatial Forecast Combination Approach with an Application to Housing Price Prediction,2020.0,,Chuan-Hua Wei; Chenping Du; Nan Zheng,"Forecast combination has been widely applied in various fields since the seminal article of Bates and Granger (1969). However, these research were focused only on time series data. Few study focus on the spatial data, this paper proposes a novel adaptive spatial forecast combination method with varying weights based on the geographically weighted regression technique. Finally, the proposed method is applied to the Boston house prices prediction, and the results indicate that our procedure performs better than the other forecast combination methods.",1.0,https://doi.org/10.5539/ijef.v12n4p11
cdbc248aa205bf4aed3f640817782cd591d8cfb9,10.1117/12.3030415,Estimation of House Property Value with Hybrid Machine Learning,2024.0,2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies,S. S. Mallika; Ch. Sarada; G. Swetha,"Numerous variables contribute to wide swings in the price of homes. Size, proximity to services, and other conveniences all play a role. However, in certain instances, these costs are not based on accurate value and are, instead, inflated. Many of us have probably come into situations in which the price of a home seemed excessively high for reasons that ultimately didn't matter to us. The goal of this work is to establish home values by considering a wide range of tangible characteristics. We need a model that takes into account all relevant factors to arrive at a fair pricing that works for both the buyer and the seller. For this reason, rather of depending on just one Machine Learning algorithm to make home price predictions, a combined approach is proposed. We can make sense of the various algorithmic outputs and formulate a rule that makes use of all these numbers. The cost of a home will be settled according to this guideline. The proposed approach would reduce errors and house price prediction precision also increases.",1.0,
3f99b0c0ab35cd5ad518c08d8d18b9d463880b1e,10.65521/ijacect.v14i1.560,AI-Based Approach using Generative Adversarial Network for Interior Design System,2025.0,International Journal on Advanced Computer Engineering and Communication Technology,J. Deshmukh; Prasad Bhokare; Pratiksha Malunjkar; Akshada Shenkar; S. Thorat,"This research paper presents an AI-based interior design system utilizing Generative adversarial Networks (GANs) for realistic textual content-to-image conversion, incorporating room dimensions and user preferences. The system leverages Black forest Labs' FLUX.1 (schnell) model, a high-pace, open-source variant optimized for rapid and high-constancy image generation. by inputting textual descriptions and spatial constraints, the model generates customized interior layout visualizations, enabling architects, designers, and homeowners to explore diverse layouts, fixtures arrangements, and aesthetics in actual-time. The proposed approach complements the performance of layout workflows by way of providing AI-driven innovative assistance, decreasing guide effort, and presenting photorealistic previews tailor-made to person specifications. This examine evaluates the device’s effectiveness in producing coherent, high-quality interior designs and discusses potential applications in architecture, real estate, and virtual staging.",1.0,
ef8d025b8ac2e6e62c13aa98306d9347999b5e65,10.4018/978-1-7998-0948-7.ch018,"Land Use, Economic Welfare and Property Values",2020.0,Sustainable Infrastructure,P. Morano; F. Tajani; M. Locurcio,"In the paper an analysis of functional correlations of property prices with the main locational and socio-economic variables, which generally contribute to define the market value of properties, has been developed. Locational characteristics are represented by the surfaces of soil used for the main functions, borrowing the logic of the system of classification of CORINE Land Cover (European Commission). The analysis has been contextualized to the 258 municipalities of the Apulia region (Southern Italy), and has been referred to two different moments (years 2006 and 2011), and two different market segments (residential and retail). The functional relationships between property prices and explanatory variables considered, estimated through a software that implements a genetic algorithm, are particularly interesting. The methodology outlined constitutes a valuable reference for the definition of models aimed at supporting, in a more rational and convenient way, public planning decisions and private investment choices.",1.0,
f0afe7a2306421dbf9243c036efd4ef8cdf1804f,10.52819/jnes.2024.36.1.127,An Economic Impact Assessment of Urban Renewal Policies in China -Focusing on Elevator Installations in Old Residential Areas of Beijing-,2024.0,The Northeast Asia Economic Association Of Korea,Xiang-Chun Zheng; Daewon Oh,"This study examines the economic and welfare impacts of China’s aging 
population and the improvement of older urban housing. Using a sample of older 
neighborhoods in Beijing, we find that elevator renovation is significantly positively 
related to second-hand housing transaction volume and price growth. These results 
suggest that elevator installation improves residential satisfaction and stimulates the 
secondary housing market. Regression analysis showed that urban infrastructure 
improvements, income growth, and urbanization had a positive effect on increasing 
second-home transactions, while China’s aging population and rising home prices 
had a negative effect. In particular, in the eastern part of Beijing, elevator 
renovations were found to further boost second-home transactions. These findings 
provide empirical evidence of the economic and welfare impacts of China’s aging 
population and the improvement of urban old housing, suggesting the need for 
continued policy optimization and support.",1.0,
701435345515507aa8fa70f12b34db85b1527cba,10.1109/conf-spml54095.2021.00033,Estimate the Housing Price Under the Impact Of COVID-19 and Possible Migration Due to the Demand for Density,2021.0,International Conference on Signal Processing and Machine Learning,Qingyuan Jiang,"Different population among the states shows a heterogeneous housing price trend during the past years. Any possible abnormal migration will cause price change. Thus, the migration could be tackled by comparing the current price trend with the data in past 10 years. COVID-19 is a strong effect which could cause migration. In order to observe the possible migration under this situation, wo high-population states were chosen as examples – California and New York, to compare with two low-population states – Nevada and Ohio. Three machine learning techniques have been used (Random Forest, XGboost, and Ridge and Lasso regression) to forecast housing price in U.S.: the difference between the real price and forecast price trend will show the amount of real estate transactions affect by the pandemic. The observed data was compared with the predicted results after COVID-19. The final result didn’t show a strong evidence that would verify a possible migration, but the answer will be clearer with further studies.",1.0,
474b2dd56d422c5715b06eb053e7391e307246db,10.1109/icrpset64863.2024.10955925,Predicting Apartment Rental Prices in Bangladesh: A Machine Learning Approach,2024.0,"2024 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET)",Hamim Reza; Nazrul Islam Tareq; Taseen Alam Dehan; Utsho Majumder Joy,"Bangladesh is a dynamically changing real estate market, and in such a world, property owners as well as tenants want to be able to predict rent prices as accurately as possible. This paper attempts to provide an inclusive machine learning solution to the prediction of apartment rental prices. Using the publicly available “8K + Property Listing Data in Bangladesh” dataset, which includes 8,000 entries with 11 unique features, we use predictive modeling to infer rental prices. The dataset provides numerous property characteristics, including location, size, and the number of beds and baths. Our findings reveal that the Extra Trees Regression model outperforms others, achieving an R2 score of 96.57%. Following closely, the XGBoost model attained an R2 score of 95.40 %, while the General Regression Neural Network (GRNN) also showed strong performance with an R2 score of 96.53 %. These results demonstrate the effectiveness of machine learning in accurately forecasting housing prices in the region.",1.0,
d20c31b1c855b9712f843362c88385e7178e263c,10.1109/iccect60629.2024.10546182,"Forecasting Housing Price Using GRU, LSTM and Bi-LSTM for California",2024.0,"2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT)",Jun Liu; Zihan Ma,"This study aims to address the challenge of predicting housing prices in California using various neural network models, including LSTM, Bi-LSTM, GRU, and a hybrid GRU-LSTM. To enhance predictive accuracy, an innovative approach involving spatial sequences based on Haversine is introduced. By considering the spatial correlation of housing prices, the data is spatialized, and the results demonstrate that the collaborative integration of spatial sequences and neural networks significantly improves model prediction accuracy. This not only provides a novel approach for the precise forecasting of individual housing prices but also holds potential to offer additional support for government oversight and regulation, benefiting homebuyers in understanding the complexity of the home-buying process. The findings emphasize the importance of considering spatial factors in housing price predictions, offering a promising direction for future research and practical applications in the real estate domain.",1.0,
38adcc87b867b6537719185fd94cb53c5ceeca55,10.1109/icet59753.2023.10374881,Predictive Modeling of House Prices; A Regression Approach,2023.0,International Conference on Emerging Technologies,Natasha Saeed; Ahsan Saeed; Maria Bashir; Lismer Andres Caceres-Najarro,"Housing prices have always been an important issue among the business community. The sudden increase in property prices has an impact on economic cycles as well as our means of living. However, understanding several features that affect residential real estate prices is still challenging. Traditional techniques have been unable to choose essential features for precise predictions, which frequently leads to reduced prediction accuracy. In this research work, we utilized machine learning techniques to increase prediction accuracy and get data-driven insights into property evaluation. This study compares Random Forest Regressor and the Decision Tree Regressor using an extensive dataset of Bangalore that includes nine property features. The findings show that our models achieved an optimal accuracy level and provide insights into the variances in model performance, assisting policymakers in choosing the most suitable approaches to get accurate house prices across various market circumstances.",1.0,
c80bc6baf2099facb6c55075a4967362226de03c,10.1111/roiw.12704,Heterogenous Rates of Return on Homes: Do The Rich Do Better?,2024.0,The Review of Income and Wealth,Edward N. Wolff,"Recent work on wealth inequality using the capitalization method wherein aggregate wealth totals are distributed proportionately to property income like dividends motivates concern about whether rates of return on assets vary across the income or wealth distribution. Here I use accrued capital gains and imputed rent on homes estimated from the Survey of Consumer Finances to address this issue. Both capital gains and imputed rent form part of total income. I find strong econometric evidence that returns vary directly with wealth level and are considerably higher for very wealthy compared to poorer households. However, I do not find evidence that higher income households receive higher returns once controlling for overall market house price trends. Returns are also strongly related to overall market house price movements, suggesting that timing the market is a key determinant. However, paradoxically, adding housing returns to baseline household wealth reduces overall wealth inequality.",1.0,
84cc4497992f80cf457a97f63d050c3843dd1ff9,10.24957/hsr.2020.28.4.109,The Impact of Real Estate Regulation on Housing Prices: Focus on the Actual Transaction Price of Apartments at Suwon,2020.0,,Koangsung Choi; M. Noh,"This paper examines the impact of real estate regulation on housing prices. A quasi-experimental assignment of the regulation at Suwon will be utilized by adopting the regression discontinuity design (RDD) model. Furthermore, we are also combining the difference-in-differences (DID) to the regression discontinuity design (RDD) model to eliminate other influencing factors that affect house prices. The results reveal that the regulation reduced prices by 3.8~5.0%. We are unable to identify a negative effect on the price that is more than 300 million won, while the houses worth less than 300 million won decreased in price by 5.6~14.2% before the regulation was applied. Although the implementation of the regulation has a positive effect on the stabilization of housing prices, our findings indicate that regulation can intensify the polarization of housing prices, and reduce the supply of rental housing. Moreover, it is possible that a balloon effect of housing prices can occur.",1.0,
b6c37cc2179253126198a3a00409f3c3ac156ea2,10.1109/bdicn58493.2023.00029,Forecasting House Resale Prices using Ensemble learning,2023.0,"2023 2nd International Conference on Big Data, Information and Computer Network (BDICN)",Zhenyu Li; Zhaoji Li,"Forecasting House sales prices is a critical issue for individual investors and government. prediction system has high accuracy and flexibility and plays an important role in decision making. However, previous studies have focused only on limited datasets, as well as nontime series or feature prediction. This paper proposes a blending model based on Python and its xgboost, DF21, and Geatpy packages to predict resale prices in Singapore. First, the high-cardinality categorical attributes are preprocessed by means of mean coding. Then, we propose a linear blending method that includes GA-HL-Xg-Boost, GARandom Forest (GA-RF), deep-Random Forest (DRF) and lightGBM, with Gini impurity to determine the importance of features. Finally, the result shows that it can obtain a mean absolute percentage error (MAPE) of 7.36% in a stationary overall trend of house prices trend. Our research can provide a powerful predictor of house resale prices in different economic environments.",1.0,
1a5a200dbc6d4af47cb6e8d5c7885680aada9563,10.1109/otcon60325.2024.10687534,House Price Prediction using Machine Learning Approaches,2024.0,2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0,Reshma; Tushar Semwal; Shashank Pareek; Shruti Kuhar,"Machine learning has grown in significance as a field of technology throughout time. This study focuses on demonstrating how machine learning and its algorithms may be used to estimate price changes in the real estate business, which is a non-direct sector of the economy of our country. In terms of pricing, real estate has evolved into one of the markets with the highest levels of competition. Our method helps determine a property’s starting price based on local criteria. Analyzing past market patterns, pricing ranges, and technical advancements will help forecast future costs. This study seeks to construct this prediction engine for actual user usage by reviewing the application of existing machine-learning techniques on two drastically different datasets. The goals of our initiative are to reduce financial loss and buy high-quality homes. The cost of a home is determined by a variety of factors, including the buyer’s budget and the number of home improvements.",1.0,
a9dd3756c8a9da7d31089cf0201faf6976973330,10.1177/27652157251396216,Effect of Professionalism on Hosts’ Pricing Strategy in Peer-to-Peer Rental Platforms: Evidence From Airbnb,2025.0,Journal of Smart Tourism,Ziying Li; Sai Liang; Rob Law,"The rapid growth of sharing accommodation improves resource utilization and spurs economic growth, but concerns arise over subjective host pricing. This study examines the relationship between host professionalism and property pricing, and the moderating role of external shocks (e.g., the COVID-19 pandemic). Using a longitudinal dataset covering all Airbnb listings in San Francisco between June 2019 and June 2022, we construct panel regression models with fixed effects to answer the research questions. The findings indicate that professional hosts set lower prices than amateurs under equivalent conditions. This negative effect is amplified for listings near popular tourist attractions. Regional COVID-19 severity significantly weakens the negative impact of host professionalism on prices. These results contribute to sharing economy literature and offer practical guidance for hosts, platforms, and regulators to understand and standardize pricing strategies.",1.0,
fadb017374e641cd4843bba0cc36b31469581842,10.1109/iciip61524.2023.10537685,Linear Regression-Based Prediction Model For Real Estate Properties in India,2023.0,International Conference on Intelligent Information Processing,Harsh Verma; Amrit Singh; S. Avasthi,"The real estate market is one of the most competitive in terms of price, and it is also one of the few that is subject to change on a consistent basis. It is one of the most significant areas where the principles of machine learning are applied to improve and increase the accuracy of cost predictions. The price of a home depends on three factors: its physical condition, its design, and its location. The current method of estimating property prices does not account for fluctuations in market prices or the rate of inflation. The objective of this paper is to provide Indian customers with a forecast of residential prices that takes their needs and budgets into account. To enhance the overall performance of the prediction models, the mean target encoding is also incorporated into the methodology. According to the findings of this study, the estimation error that is produced by linear regression is the lowest and accuracy of the implemented system is 98.6%.",1.0,
2d3a81176d43e7136a171c0bfd15b8e2502b8bdd,10.1080/20507828.2022.2105573,From Gentrification to Sterilization? Building on Big Capital,2022.0,Architecture and Culture,A. Minton,"Abstract In a period of extreme inequalities, the speed and scale of capital flows into London constitutes a new economic process qualitatively different from that of gentrification. It is underpinned by the financialization of housing introduced in the 1980s, the policy of Quantitative Easing, the influx of corrupt money into the city and the growing role of private equity in real estate markets. Since the 2008 financial crash the “trickle-down” of land and property price rises has built on the existing shortage of affordable housing to create an acute crisis. Combined with the commercialization of housing benefit, inflationary pressures both top-down and bottom-up are the consequence. While the current influx of capital shares key characteristics with economic gentrification, its speed and scale is unprecedented. I propose that, since the 2008 crash, we have been witnessing a new phenomenon, which I liken to sterilization rather than gentrification.",1.0,
bd4437daf3848828ea0bb1b6e0b2bbcf9a2ea862,10.23939/iw_itpm2024.077,A machine learning method for real estate operation projects forecasting,2024.0,International Workshop IT Project Management,S. Bushuyev; D. Bushuiev; Nikolay Poletaev; Mykola Malaksiano; Dmitriy Kravtsov,"The study examined the impact of heterogeneous urban night lighting on the possibilities of
improving the forecast accuracy for real estate operations projects in a large city. By transforming
high-resolution night satellite images into light clusters, new dataset features were obtained after
calculating the centers of light clusters and the distances between them. The study used a dataset
on real estate rentals in Houston, Texas, USA. The light clusters were linked to the terrain based
on their geometric coordinates obtained using QGIS. These new features were integrated into a
machine learning model based on the LightGBM regressor. Calculations showed that the
reduction in forecast error (in the mean squared error metric, MSE) for our dataset was 11.8%,
significantly exceeding the influence of other features investigated in the study. The results
suggest that the “light” feature can be considered highly promising for real estate operations
projects.",1.0,
719db44873381028e7b7229f5c71ff278d8f22fd,10.1109/icac3n56670.2022.10074011,Prediction of House Price Using Stacked LSTM Model,2022.0,"2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)",Shruti Goswami; V. S. Bramhe; Shaveta Khepra,"House price is used as an economic indicator. Prediction of house price is important as it can be helpful for those who want to buy or plan to buy a house in the future or near future. It can give them an idea of the future price. It is also be helpful for sellers who are willing to sell their house as they can understand the future trend of the price of their houses. House price prediction is beneficial for the investors who want to invest in real estate. It helps to know the future trends of the real estate of a locality. In this paper, we have used deep learning Long Short-term memory model to predict house price. This model performed quiet well. Here our main motive is to see how data size influences a deep learning model. In a single model we have used different data size (specifically 12 different data sizes) and compared the results. It is evident from the results that data size plays a significant role in our model’s performance.",1.0,
3c304f8406f95b0529b93977f78882d6777c8c62,10.48550/arxiv.2409.02130,From Predictive Importance to Causality: Which Machine Learning Model Reflects Reality?,2024.0,arXiv.org,Muhammad Arbab Arshad; Pallavi Kandanur; Saurabh Sonawani; Laiba Batool; M. U. Habib,"This study analyzes the Ames Housing Dataset using CatBoost and LightGBM models to explore feature importance and causal relationships in housing price prediction. We examine the correlation between SHAP values and EconML predictions, achieving high accuracy in price forecasting. Our analysis reveals a moderate Spearman rank correlation of 0.48 between SHAP-based feature importance and causally significant features, highlighting the complexity of aligning predictive modeling with causal understanding in housing market analysis. Through extensive causal analysis, including heterogeneity exploration and policy tree interpretation, we provide insights into how specific features like porches impact housing prices across various scenarios. This work underscores the need for integrated approaches that combine predictive power with causal insights in real estate valuation, offering valuable guidance for stakeholders in the industry.",1.0,
752564cdb552ae2068b7197cd7e2fe2ef503224f,10.1109/icears56392.2023.10085090,Machine Learning Algorithms for Predicting House Prices in a Smart city using its Real-Time Data,2023.0,2023 Second International Conference on Electronics and Renewable Systems (ICEARS),M. Joel; M. Navaneethakrishnan; K. Sriram; S. Manonmani,"Housing prices are an important economic indicator, wherein the property values are important to both buyers and sellers. One of the least transparent industries across the world is real estate. In order to conduct the research, real-time housing data has been collected from Chennai City. Sine this study deals with the continuous outcome variable, regression techniques are highly preferred. The major goal of this research study is to predict housing values by using real-time information. This study performs various house price evaluation process based on various factors. Several regression models like Lasso and Ridge are utilized and their performances are compared in order to select the best model for resolving a specific issue. This study intends to develop an effective machine learning model to predict the price of a property based on input parameters and display the information in a website to reach out to people.",1.0,
518f84ffc2f5e7f4cdc678100c49e9f29ac8a661,10.4018/ijagr.2021040102,The Impact of Natural Amenities on Home Values in Western Colorado,2021.0,,Nathan w. Perry; T. Casey; T. Parece; Cory J Castaneda,"This paper investigates the impact that proximity to natural amenities has on improving home values in Mesa County, Colorado. Controlling for standard home characteristics, the study investigates the value to homes of the proximity to trails, the Colorado National Monument, Bureau of Land Management Land, golf courses, the Colorado/Gunnison River, open space, and public parks, using ordinary least squares, fixed effects (controlling for time and zip codes), and a spatial error model. GIS is used to determine distances for the spatial econometric model. Each amenity is evaluated at 250, 500, and 1000 meters. The results show that homes located within 250 meters of a trail sell for 4.45% more, homes located within 500 meters of BLM land sell for 9.07% more, homes located within 250 meters of a golf course sell for 12.70% more, and homes located within 250 meters of the Colorado National Monument sell for 12.90% more.",1.0,
c0285c8d75a4a7cd52a5fcfa6f7b18560e6bda34,10.64361/14445921.2026.212325,Macroeconomic Determinants of House Prices in Urban Ghana: Evidence from Valuation Data,2026.0,Pacific Rim Property Research Journal,K. Baako; Kwabena Mintah; Vanessa Glover-Quartey; Ernest Amankwah; Boakye Dankwah,"While the determinants of house prices have enjoyed much research attention spanning many decades, the impact of macroeconomic variables has seen significantly less research. Of the studies that do exist, only a few cover developing markets, and the results from these studies are insufficient and controversial. In this paper, we examine the macroeconomic determinants of house prices in a developing economy - Ghana. Using primary empirical data on house prices, we examine the impact of macroeconomic variables on house prices in Accra, Kumasi and Urban Ghana (an aggregate of the two markets). We find strong evidence of cointegration between macroeconomic factors and house prices in Kumasi. The study reveals that in the long run, while Kumasi’s house prices are influenced by population growth, GDP growth, interest rates and inflation, prices in Urban Ghana and Accra are only influenced by population growth.",1.0,
3a7ce89dccd88ec3ee0fc51171fe9b601afa984e,10.1177/0160323x231168613,The Vertical and Horizontal Equity of Property Assessment Caps,2023.0,State and Local Government Review,Mary O. Borg; J. Borg,"Do assessment caps make the property tax more regressive? Are they fair to all taxpayers, or do some groups get more benefit than others. This paper examines these issues using a sample from the Jacksonville, FL Metropolitan Statistical Area. Regression models are run using housing values averaged at the census block level and matched to census block socioeconomic and demographic data obtained from the US Census. Results show that when permanent income is used as the measure of ability to pay, the SOH benefit makes the property tax fairer, but when current income is used as the income measure, it makes the tax less fair. In addition, the assessment caps cause some serious horizontal inequities. For, example, homes in 100% Black census blocks receive, on average, 6.6 percentage points less value from the SOH benefit relative to their home’s value than the homes in 100% White census blocks.",1.0,
691a54d7a71befee9d921962fb33c6d730b6d18f,10.1145/3453187.3453321,Factors Influencing Housing Prices: An Empirical Analysis From New York City,2020.0,EBIMCS,Dingrui Dong,"This paper studies the determinants of housing price dynam- ics in the New York City. This model is used to study the effect of house factors on the house prices appreciate rate. Using the records of real estate transactions between 2003 and 2017 in the New York City (short referred to as NYC), this paper found that the influencing factors of the property itself have different effects on the house prices appreciate rate. After removing the factor of real estate location, for the average house price appreciate rate, the number of real estate transactions has the greatest impact on house prices; in the case of different house price levels, the land area of the house has the greatest influence on house price growth dur- ing the boom and the depression of housing prices, while the structure and land area of the real estate does the greatest during the recovery period of housing prices",1.0,
378bd5318a998eee2957848dcb751215e77b26a8,10.1109/cds52072.2021.00087,Prediction of House Price Index Based on Machine Learning Methods,2021.0,2021 2nd International Conference on Computing and Data Science (CDS),Ze Li,"A house price index (HPI) is significant for people to receive accurate information such as banks, financial departments, real estate industry investors, and home owners. Data from Kaggle website by using neural network and regression models, such as linear, ridge Lasso regression. There are 99326 samples. We explore the relationships between factors of frequency, HPI flavor, HPI type, HPI index, level, period, place id, place name, and the year of houses sold or rent. In terms of the accuracy of the prediction, the accuracy of BP neural network is slightly better since the value is smaller than other two regression prediction both on the training set and testing set. However, better model like XGBoost could be chosen to improve the prediction result. Since an international concern about house prices raises recently, the precise calculation of house prices is important as well.",1.0,
b45ba2a7d39afb1478a67deccfb5c13268a7b185,10.1109/icmete.2018.00018,Factual Dimension Identification and Usage for Real Estate Framework,2018.0,International Conference on Micro-Electronics and Telecommunication Engineering,Gursimran Kaur; Harkiran Kaur,"Real estate systems hold a lot of information that can be mined for useful patterns. This paper aims to build a Key Performance Indicators (KPIs) to evaluate the best feature for classification of real estate data. ANalysis Of VAriance (ANOVA) and correlation coefficients are used in this paper to evaluate the key features in the database. The main objective of KPIs is to select the most relevant quantitative factors and finding the various key features such as Length, Breadth, Area, Collector rate and Commission per sale with House price index attribute. Extensive analysis have been done to evaluate a set of metrics that enable the performance management of a real estate system. It has been observed that the selected features rapidly increases with the house price index, therefore are more useful to improve the accuracy of machine learning models.",1.0,
d8bb5ab5f11f3763d0620e3c189547e89a43ab74,10.1109/iccigst60741.2024.10717472,Going the distance: Application of Machine Learning Algorithms in predicting Housing Price Dynamics,2024.0,2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST),Babita Sonare; Sudeep D. Thepade; Pratiksha Gangane; Esha Govardhan; Tisha Nawani; P. Gadkar,"In this paper, we have used various algorithms such as Gradient Boost Regression, Catboost Regression, Decision Tree Regression and K-neighbors Regression by categorizing predictions based on threshold. To address the problems of data quality and other complexities, we performed validation on all the algorithms mentioned above. Predicting house prices often involves many barriers such as taking all the important factors into account, being aware of the demand of a particular area, government policies, spatial locations and houses with different sizes. We overcame these problems with the ML models we built. Among all the algorithms, Catboost Regression gave great results with mean absolute percentage error of 0.018 and r-squared value of 0.904. This paper provides insights in more detail about the strong positive correlation between area and price.",1.0,
5604257b0816bf097e65107bc1857e085e62449e,10.1109/idciot56793.2023.10053480,ML-based Java UI for Residence Predictor,2023.0,2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT),Saleha Saudagar; Mukund Kulkarni; Isha Raghvani; Haider Hirkani; Inderdeep Bassan; Pranav Hole,"When people consider buying a home or property, they first tend to go online to try trends and other related research. The proposed system presents a console application in Java that can predict house prices using machine learning algorithms based on data mining concepts that extract relevant knowledge from raw data and perform practical analysis. The system proposed here analyzes related properties for predicting the prices. Using the wide varieties of libraries available in Python, the proposed system attempts to implement a way to create user interfaces in Java by interacting with Java and Python machine learning model. Random Forest Regression model has been used to predict the prices with an R-squared score of 86.72%. The proposed system implements a JAVA graphical user interface using Swing technology which is simple and lightweight.",1.0,
019dcbed2177b0821fa32b9e0be094225713210f,10.1109/sceecs64059.2025.10940122,Property Price and Rent Prediction System,2025.0,"2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)",A. Singh; Abhinav Rai; Devansh Karnwal; Anupam Vats; Tripti Pandey,"Machine Learning (ML) has revolutionized various sectors including speech recognition, product recommendations, and healthcare. This research focuses on applying ML techniques to address challenges in real estate market prediction. The unpredictable nature of property prices creates significant difficulties for buyers in making informed decisions. This research proposes a multi-algorithm housing price prediction model, integrating SVM, Random Forest, and GBM approaches, with Random Forest demonstrating superior predictive performance. The model considers various factors influencing property prices, enabling buyers to make data-driven decisions without excessive reliance on intermediaries. This paper presents the methodology, implementation, and comparative results of these algorithms in the context of real estate price prediction.",1.0,
f49964fbd5288e4080d6312bd49d893b2b5afa1e,10.1080/08882746.2023.2301206,"Are younger buyers driving the housing boom? A geographic perspective on US home purchases, 2018-2021",2024.0,Housing and Society,Nicholas Kacher,"ABSTRACT Rapid growth in US housing prices has coincided with the large Millennial demographic group reaching peak homebuying age, leading some to speculate that new demand from younger buyers is putting upward pressure on home prices. This Research Note uses data from the Home Mortgage Disclosure Act mortgage data to examine where younger buyers are purchasing homes. Data show that the share of buyers under age 35 fell in more than half of US counties, including 45% of metropolitan counties. A county- and MSA-level regression analysis finds a null or slightly negative correlation between changes in the share of buyers under age 35 and changes in home prices, and a small positive effect on debt-to-income ratios from 2018 to 2021, providing little to no evidence that young buyers are pushing up home prices.",1.0,
a1f36eb885c0468bb91b36bd860a9dd4d42e8d5a,10.52372/kjps13003,The Effects of the Flat Tax Reform Proposal an the U.S. Housing Market,1998.0,Korean Journal of Policy Studies,Keakook Song,"This paper analyzes the effects of the Armey-Shelby tax reform proposal among the flat tax proposals on new owner-occupied housing market in USA on the basis of partial equilibrium. The effects in the short- and long-run are examined based on the Kenneth T. Rosen's regression result focusing on the effects of the user costs on home ownership. The Armey-Shelby plan would cause housing price in the short run to decrease with unchanged quantity because of nondeductibility of mortgage interest and property tax payments in the short-run. But the plan would cause housing market in the long run to be more activated, that is, increase in housing quantity and decrease in housing price, through lower interest rate caused by untaxed savings and investments that mean tax neutrality against savings and investments.",1.0,
e8e5deb90a4d93e340847f95014ef4457dd98db8,10.1016/j.dib.2024.111141,Unlocking insights in real estate markets: Integrating geo-spatial data for comprehensive property valuation and urban planning,2024.0,Data in Brief,Serkan Varol; Serkan Catma,"This article summarizes a database for analyzing the impact of structural and environmental characteristics on residential property values in Hamilton County, TN. The original dataset consists of house characteristics data for 873 residential sales between January 1, 2023, and September 25, 2023. Using Googleʼs API tools and Point to Edge computations, several geographical variables—including the distance to green recreational areas, surrounding facilities, restaurants, air quality index, walk score—were gathered. The aggregated information can help forecast Hamilton County's housing market with accuracy and correctly assess the environmental impact on housing prices. More specifically, the value of any environmental amenity in the study area can be implicitly estimated using this dataset.",1.0,
300b584d637ac8516983e443fec9bc6112704424,10.1111/boer.12468,How rising housing prices affect labor costs in China: Heterogeneity and lagging,2024.0,Bulletin of Economic Research,Pengfei Yuan; Yunhui Cao,"The impact of house prices rising on urban labor costs has typical heterogeneity and lag effects, but there is little literature on it. Based on the panel data of 283 prefecture level cities from 2001 to 2017, we constructed panel model considering lag effect and threshold effect. The results show that lag one period of housing price has significant positive impact on urban labor costs, with the increase of housing prices, the effect of pushing up the current labor costs is gradually increasing. So, this impact has obvious lag effect and threshold effect. Through grouped regression to test heterogeneity effect, at the regional level, the heterogeneity of the impact is more significant. The more developed the economy is, the greater the pushing effect of house prices on labor costs.",1.0,
4fd2f155d8b84454913ebc2404ff2a757ad176ac,10.1109/iccc54389.2021.9674495,An Evaluation Model of Demolished Houses based on Improved Deep Learning,2021.0,2021 7th International Conference on Computer and Communications (ICCC),Haifeng Lv; Bao-Ming Pu,"In view of the problem of housing price evaluation, the traditional methods are not objective and efficient, which often stimulate the contradictions of various stakeholders, and even cause social chaos. This paper proposes an evaluation model of demolished houses based on improved deep learning, applies the theory of deep learning to the evaluation of demolished houses, optimizes and improves it, adds forgetting unit to it, and constructs regional factor index system, individual factor index system and transaction based index system. In this paper, a residential area in Shenyang is taken as the evaluation object, and the experiment shows that the proposed method improves the evaluation efficiency of demolished houses and reduces the subjective factors in the evaluation process.",1.0,
3816f0b120aa18a8235b6f88c91dc5595fca3e5d,10.1109/mlbdbi54094.2021.00046,Guangzhou House Price Analysis based on Machine Learning Methods,2021.0,"2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)",Weizheng Liang; Le-Yu Pan; Jia Sun; Jia-Nuo Wang,"With the continuous development of China's economy, Guangzhou, one of the four most powerful cities in China, has attracted many people to go and settle down there. This makes housing demand in Guangzhou continue to rise and has led to higher housing prices in recent years. In this paper, we aim to exploit the main factor, which affects the house price. Due to that, we are not able to obtain the new-house transaction. We start with second-hand housing to find the relationship. We utilize TF-IDF and K-means to analyze the data, and we conclude that the housing type is mainly related to its price, which two or three bedroom houses are with high demand and price. In contrast, houses with only one bedroom are not popular in the housing market at a low price.",1.0,
8b017b4528a8b9fab27104a8af1538d95ab2ca8c,10.1109/urtc65039.2024.10937620,An Explainable AI Framework with ML Model Stacking for House Price Prediction,2024.0,2024 IEEE MIT Undergraduate Research Technology Conference (URTC),S. Ganapathy; Tingyu Li,"The post-COVID-19 real estate market has presented challenges in finding affordable housing. Accurate house price prediction systems can aid in addressing this issue. This research develops a stacking regression model framework for improving predictive performance with the consideration of Explainable AI (XAI) of house price estimation. This new model was subsequently compared to Regression-based prediction models and a Neural Network to assess model performance. The Stacking Regressor’s strong $r^{2}$ value of 0.901 and its noticeably lower MAE and RMSE highlight strong precision. XAI techniques, particularly SHAP values, were employed to enhance model interpretability, offering insights into individual feature contributions.",1.0,
c335e16627a261fa9397ed6c2e942f91c71d8948,10.1109/arace56528.2022.00035,Research on Submarket Effects of Real Estate Valuation Based on Bayesian Probability Model. A Comparison Between Cities,2022.0,"2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE)",Xinjing Qin; Ping Zhang; Xinyang Zhang; Bin Cheng; Xianglin Bao,"Submarket effects are essential for real estate valuation since they could be used to increase both the prediction accuracy of housing prices and the interpretability of the machine learning model. In this paper, a Bayesian probability model that divides the housing market based on the housing location is proposed to forecast house prices, and discover key factors in house prices. A comparison of the key influencing factors affecting the real estate market in Hangzhou and in Chengdu is provided. The experimental results show that the key influencing factors in corresponding functional areas of different cities are similar, which sheds a light on creating a unified model for the real estate valuation.",1.0,
357a9063d77609f7e7c5a7eedd6c092a06c711cc,10.1109/sces61914.2024.10652318,Enhancing Zillow Zestimates: Leveraging Machine-Learning for Precise Property Valuation Predictions,2024.0,Students Conference on Engineering and Systems,Avirup Banerjee; Aditya Pandey; B. Prakash; Saptorshi Banerjee; Atri Bandyopadhyay; Prasun Chakraborty,"A variety of machine learning techniques, such as Linear Regression, Lasso Regression, Ridge Regression, Elastic Net, Decision Tree, Random Forest, and Gradient Boosting, are used in this work to evaluate the assessment of house worth. We want to improve the precision and dependability of house price forecasts by utilizing a variety of approaches. We compare how well these algorithms capture intricate correlations between different variables and home prices. By doing extensive testing and analysis, we aim to determine the best real estate value model, advancing predictive analytics in the housing industry. Our results will help buyers, sellers, and real estate agents make better-informed decisions.",1.0,
3e02f40124714e937f79164ce91d5de7a5359b45,10.1109/icot51877.2020.9468726,Research on House Price Forecast Based on Hyper Parameter Optimization Gradient Boosting Regression Model,2020.0,International Conference on Orange Technologies,Xiaogang Wang; Shichen Zhai; Jui-Le Chen,"In order to study the prediction accuracy of different regression models, this paper selects housing price prediction as a typical research object, and proposes a hyper parameter optimization gradient boosting regression model (HPOGBR). Firstly, the data is preprocessed and the key features are extracted through visualization and basic model pretraining. Then, multiple machine learning regression models are used for comparison with our HPOGBR model base on evaluation indicators. Finally, the optimal model and parameters are selected by grid search. The experimental results show that our proposed HPOGBR model has better accuracy and convergence over existing machine learning regression models.",1.0,
d4b82e34d799044cfc3abbe13a0e5718709300f2,10.1109/ssaic61213.2024.00092,Model Research on Forecast of Second-Hand House Price in Shenyang Based on XGBoost Algorithm,2024.0,"2024 Asia-Pacific Conference on Software Engineering, Social Network Analysis and Intelligent Computing (SSAIC)",Kan Lin,"In order to have a deep understanding of second-hand house prices in Shenyang, this paper studied transaction data of second-hand housing from the platform ke.com. First, the qualified data was determined and collected, then processed and data parameters were set. Second, by comparing three models, Ridge regression, Lasso Regression, and XGBoot model to find which is the best model to be used in the second-hand housing market. By implementing the data and analysis with visualization graphics, the study results indicate that the XGBoost model offers more accurate predictions in understanding second-hand housing prices in Shenyang.",1.0,
d024cc05ea9de680b925af59108c7f5662f6b08a,10.1109/cds49703.2020.00085,Predictive Modeling of U.S. Housing Prices Reveals Key Indicators of Real Estate Prices and Economic Health,2020.0,2020 International Conference on Computing and Data Science (CDS),Wenjing Yin; Xinyuan Zheng; Xuan Zhu,"Housing market is one of the key sectors of the economy. Understanding and forecasting changes in the housing market is therefore essential for an economic analysis. This work analyzes trends in housing prices and related variables using predictive linear and random forest models. Key variables related housing prices are identified and successfully used to predict housing prices for out of sample forecast. The models presented in this study are valuable tools to evaluate housing market trends, and they can be easily extended over time because of their ease and efficiency of use.",1.0,
c6ab53169b6675e2dc378d7c2b42ba29826c52e2,10.22394/1993-7601-2023-70-72-88,A bubble in the housing market? Analysis based on the repeat-sales method for the secondary market in Moscow and Saint Petersburg,2023.0,Applied Econometrics,Anastasia Kosyakina,"A modification of the repeat-sales method is proposed, which allows estimating housing price indices taking into account changes in the qualitative characteristics of the properties being sold over time, as well as limitations in Russian data. The results of evaluation of indices for Moscow and St. Petersburg are given. It is concluded that in real terms and taking into account the change in the quality of purchased objects, the price levels for the secondary housing in these cities at the end of 2021 are comparable to the level of the end of 2012.",1.0,
5d797e7c3e5f84a3d32402072a64ec260ecfd1e1,10.64361/14445921.2024.212309,"The impact of zoning reform on property prices within lower Hutt, Wellington",2025.0,Pacific Rim Property Research Journal,Ishan Parbhu; Hassan F. Gholipour; Sharon Yam; Kamel Taoum,"Zoning regulation can have a significant effect on property values by restricting types of development in a particular area. By focusing on the implementation of Hu> City Council’s District Plan Change 43, this study examines the effect of zoning changes on residential property prices in Lower Hu>, New Zealand. Using transaction records of 100 residential properties and employing multivariate regressions, the results show that properties affected by the zoning reforms experienced lower appreciation in value compared to unaffected properties.",1.0,
c848aba62c741d1da852b57c249479ac9592545e,10.22214/ijraset.2021.34804,Review on House Price Prediction using Machine Learning,2021.0,International Journal for Research in Applied Science and Engineering Technology,Monika Sahu,.,1.0,https://doi.org/10.22214/ijraset.2021.34804
b9fb7c6cd2bfb2eba65f1ef6a05576cb289aa644,10.2457/srs.34.397,Appropriate Valuation of Housing Using a Hedonic Price Function and Disclosure of Information,2003.0,,Keiko Nosse Hirono,,1.0,https://doi.org/10.2457/srs.34.397
e4411ae0de611ce218c067e51a6ff3f375746960,10.6107/jkha.2014.25.6.039,The Influences of Apartment Complex Characteristics on Housing Price by Hierarchical Linear Model,2014.0,,Keonggu Hong,,1.0,https://doi.org/10.6107/jkha.2014.25.6.039
1dda013c837d038176b68d4da3be24c9f26e2787,10.56726/irjmets34127,FORECASTING HOUSE PRICE INDEX IN TURKEY USING ARIMA TRANSFER FUNCTION AND ARTIFICIAL NEURAL NETWORKS,2023.0,International Research Journal of Modernization in Engineering Technology and Science,,,1.0,https://doi.org/10.56726/irjmets34127
acd945a225c6801bacec2b1df9418c4067093ec2,10.25236/ajbm.2023.050207,Machine Learning-Based Analysis of Housing Price Predictors,2023.0,Academic Journal of Business &amp; Management,,,1.0,http://francis-press.com/uploads/papers/l47TQMFSc1bDDJ4GVFsLrVf73NxkWEWVL9HF98us.pdf
4d495eac2da3050de423475cadc63d50355bea75,10.18178/jaai.2025.3.1.1-18,Let’s Boost House Price Predictions: A Machine Learning Approach for Norwich,2025.0,Journal of Advances in Artificial Intelligence,,,1.0,https://doi.org/10.18178/jaai.2025.3.1.1-18
5643cd081c3df44f2002a85bd0fd1681f08fe6eb,10.1016/j.cities.2026.107266,Explainable machine learning for urban housing prices: Policy relevant diagnostics across three cities,2026.0,Cities,Youngre Noh; Chan Young Park,,1.0,https://doi.org/10.1016/j.cities.2026.107266
8802a8ce5b544ffc926bde91241db94e5da9e147,10.3934/math.20251058,Learning dynamics and convergence of machine learning driven neural ordinary differential equations model for housing price prediction,2025.0,AIMS Mathematics,Zhikun Luo,,1.0,http://www.aimspress.com/aimspress-data/math/2025/10/PDF/math-10-10-1058.pdf
74d547d03ce09414cd47d49258d08d24dec223de,10.7465/jkdi.2017.28.1.173,Application of geographical and temporal weighted regression model to the determination of house price,2017.0,,Saehee Park; Min-soo Kim; J. Baek,,1.0,http://koreascience.or.kr:80/article/JAKO201711656576813.pdf
2bc5488e24b916f691201ab5eea7753c17e6634b,10.30902/jrea.2025.11.1.179,"An Analysis of Housing Price Characteristics in Areas Approved and
 Announced for Management Planning in Seoul’s Moa-Town",2025.0,Journal of Real Estate Analysis,S. Lee; Han-Ro Seung,,1.0,http://www.ejrea.org/download/download_pdf?pid=jrea-11-1-179
e85a70d80c8be7df5bb231edecac21f80be1f8eb,10.1063/5.0239036,House price prediction with advanced machine learning techniques,2024.0,AIP Conference Proceedings,P. Saraswathi; M. Prabha; N. A. Jhoshitha; J. G. Prathiksaa,,1.0,http://dx.doi.org/10.1063/5.0239036
b9e94cc5a90b6472a16be368f1a7bbb01e160d6a,10.4108/eai.2-12-2022.2328696,Impact of Air Quality on Housing Prices: A Machine Learning Approach,2023.0,"Proceedings of the 3rd International Conference on Big Data Economy and Information Management, BDEIM 2022, December 2-3, 2022, Zhengzhou, China",Zulin Feng; Xinlong Li,,1.0,http://eudl.eu/pdf/10.4108/eai.2-12-2022.2328696
c8005d27e59d7d848f2d85545764c2383d3b971c,10.9728/dcs.2022.23.12.2445,Development of Rent House Price Prediction Service based on Machine Learning,2022.0,Journal of Digital Contents Society,Kwon-Woo Lee; Soualihou Ngnamsie Njimbouom; Jeong-Dong Kim,,1.0,https://doi.org/10.9728/dcs.2022.23.12.2445
25e869cd520053b7cfecc97bc31ece95e4a5538c,10.30902/jrea.2022.8.1.23,"Impacts of Population Extinction Risk and Unsold New Housing on
 Regional Housing Prices according to Urban Spatial
 Characteristics",2022.0,Journal of Real Estate Analysis,Hyejin Kim; Wonseok Seo,,1.0,http://www.ejrea.org/download/download_pdf?pid=jrea-8-1-23
a6809c22f61c5adc84d96325ddb94c578ae5671f,10.1016/j.procs.2025.09.230,Regression models for house price analysis,2025.0,International Conference on Knowledge-Based Intelligent Information & Engineering Systems,Urszula Boryczka; Mariusz Boryczka,,1.0,https://doi.org/10.1016/j.procs.2025.09.230
133a95ca47bfe525e626ce7eb4415e3a95b28962,10.1007/s10037-025-00227-1,Validating spatial dynamics for energy efficiency in the Belgian residential rent market,2025.0,Review of Regional Research,Alesia Gerassimenko; Ian Lenaers; L. De Moor,,1.0,https://biblio.vub.ac.be/vubirfiles/134868936/118602548.pdf
c1e5e2e2b9fedd42bb0685e00654a9517fe9b5b7,10.2139/ssrn.2100495,Interjurisdictional Capitalization of a New Metro Line on Housing Values,2012.0,,Claudio A. Agostini; Gastón A. Palmucci,,1.0,https://doi.org/10.2139/ssrn.2100495
3b17c175fc7f33e8c6315201e51fb05cca078290,10.1007/s11146-015-9513-4,How Much Is a Good School Worth in Beijing? Identifying Price Premium with Paired Resale and Rental Data,2015.0,Journal of real estate finance and economics,Si-Qi Zheng; Wanyang Hu; Rui Wang,,1.0,
bc308b21c8dd70acbe73301e4fd2377b6dee99e6,10.1007/978-3-030-48465-1_56,A Model Validation and Predicting the Rental Values of Residential Properties Using Logistic Regression Model,2020.0,"Collaboration and Integration in Construction, Engineering, Management and Technology",T. B. Odubiyi; A. I. Ugulu; O. Oshodi; C. Aigbavboa; W. Thwala,,1.0,
5f71ea9cc14ebc4faad6ac1550425b029f971585,10.1117/12.761791,The study on adaptive spatial sampling used in allocation of housing price monitoring sites taking Wujin section of Changzhou city as an example,2007.0,GEOINFORMATICS,Zi-Yu Chen; Man-Chun Li; Zhenjie Chen,,1.0,
f921b7fccb7005698623728f4733ec4c71394770,10.1007/s10901-025-10264-4,Modelling and predicting house price indices in Canada via a hybridisation of machine learning methods,2026.0,Journal of Housing and the Built Environment,Iliess Zahid; Bernardina Algieri; Arturo Leccadito; R. Mamon,,1.0,
6b241feeb364dcce50a9f03065d861fc49ade0e3,10.22423/kreus.2017.9.2.151,"The Influence of School District Characteristics on Housing Prices -Evidence from Beijing, China-",2017.0,,Kijung Kim; Yao Yao; Seonju Song,,1.0,
6a217ba877ebd8cd5f2a7ef827179015a6baad48,10.1145/3157754.3157780,Macroeconomic Determinants of China's Housing Prices,2017.0,ICEME,Pei-Wen Li,,1.0,
6ef81330a7f0cacb5895b9044b9186752b8b6ca3,10.23843/as.21.2.5,Multi-Level Effect of Public Development Characteristics of the New Towns in Korea on Housing Prices Using Hierarchical Linear Model,2022.0,Appraisal Studies,J. Oh; Wonho Seo,,1.0,
e47607abc037df7d0c313fc6915c93afe9ccdc35,10.1080/12265934.2026.2642349,Economic value of neighbourhood perception on housing price: applications of unstructured multimodal big data and deep learning techniques,2026.0,International Journal of Urban Sciences,Hyebin Kim; Suhyun Lee; Juhyeon Lee; Junseo Lee; Sugie Lee; J. Han,,1.0,
d9fa106183aded621bf9bd1360b0c270a09b8388,10.1049/icp.2023.1820,House price prediction using random forest techniques: a comparative study,2023.0,"International Conference on Green Energy, Computing and Intelligent Technology (GEn-CITy 2023)",S. S. Jamil; S. Bansal; L. Vinjamuri,,1.0,
e1c7e9d11d1d6278657df06ec19b746641e91d07,10.53383/100412,Using Machine Learning Regression Algorithms to Predict House Prices in Vietnam,2025.0,International Real Estate Review,,,1.0,
8894dd807b2cfee518f061bb78e6e7efbe6ccae6,10.1007/s12061-025-09759-y,"Spatially Heterogeneous Effects of Flood Hazards on Housing Prices: A Case Study of Nanjing, China",2025.0,Applied spatial analysis and policy,Yi Chen; Tao Liu; Bi-Feng Jiang; Yujun Qi; Yiiqun Zhang; Yuan Li; Yong Lei; Jing-Xian J. You; Yao Meng,,1.0,
29399980a79a3cc83a2d476a1ce2f95351454956,10.1007/s41685-024-00337-7,"Measuring spatial heterogeneity of air quality on apartment transaction prices in Seoul, South Korea",2024.0,Asia-Pacific Journal of Regional Science,Dongwoo Hyun; H. Lee,,1.0,
52c8fa55a0ed0bb40c1e037ca9872f9c71cb03c7,10.17208/jkpa.2017.04.52.2.155,"A Study on the Correlation between Characteristics of Crime and Space Syntax, Multifamily Housing Price",2017.0,,H. Kim; Yeol Choi,,1.0,
8f72296c18778095ffd4f8c8eb7f7daada213995,10.1166/asl.2017.9864,Determinants of Housing Price in ASEAN-5 Countries: Panel Data Approach,2017.0,,Ai-Yee Ooi; Aminah Shari; Ricky Jores,,1.0,
9e46606f7b873f0756426c87bc65176f8ff1ec17,10.15396/eres2006_204,The Fundamental Determinants Of House Price Developments In Industrial Countries,2006.0,,P. Gilles; Karsten Junius; Gunnar Meyke und Subroweit,,1.0,
a9be06db7ea0495065c6debd81a5ac7de60169f2,10.2139/ssrn.2438334,The Determinants of Housing Price Dynamics: Securitization versus Investor Demand,2014.0,,Yian Liu,,1.0,
bbc3f71e886e62ef93f84a71606313153e007821,10.1080/10835547.2016.12091762,"Home Equity, Home Value, and Determinants of Mortgage Defaults During the Credit Crisis",2016.0,,F. Sabry; Ignacio Franceschelli; D. Claxton,,1.0,
99bc73e746c604b97c47dc731713e4829edc4e0a,10.1063/5.0229736,House price prediction and regression evaluation metrics score using Machine Learning and Deep Learning model,2024.0,AIP Conference Proceedings,G. Venkatesh; B. Rajashekar; Ashwini Kodipalli; Trupthi Rao; Kumaraswamy Siddagangaiah,,1.0,
98a4a425e617331c573c6afed69795f67664625a,10.2139/ssrn.1259938,Recent Trends in Home Prices: Differences across Mortgage and Borrower Characteristics,2008.0,,A. Leventis,,1.0,
be2acc6f8ff66e14ab23b9409759601e62501bcf,10.1002/jcaf.22476,The determinants of residential property prices in Japan: Analyses of different monetary policy regimes,2020.0,,Takayasu Ito,,1.0,
be36f220f0b46419f8ddc07e017a92f5689e995d,10.2139/ssrn.3601507,House Price Forecasting Based on Hybrid Multi-regression Model,2020.0,Social Science Research Network,Shivdutt Vishwakarma; Swasti Singhal,,1.0,
b811571347dea0a67edce3dba6a7e116324b0640,10.1142/s1793005708001021,GP VERSUS GLS SPATIAL INDEX MODELS TO FORECAST SINGLE-FAMILY HOME PRICES,2008.0,,Mak Kaboudan,,1.0,
7d7686d660649f020e961e479e2e0f3a7fb4d444,10.7916/d8-mawq-bg18,Determinants of Housing Prices: Evidence from thirty-five cities in China,2020.0,,Yingwen He,,1.0,
8a10afbba0fb0c5e4ef5144fe0adff886780ea8b,10.1142/s1752890925500205,"Forecasting Residential Property Prices in Jiaxing City, China: A Hybrid Machine Learning Framework Integrating Gaussian Process Regressions and Bayesian Optimization",2025.0,Journal of Uncertain Systems,Bing-Zi Jin; Xiao-Jie Xu,,1.0,
3f257566c8d450c808d8a8d78a188f5e7829f356,10.22313/reik.2020.18.1.49,Prediction of Housing Price Using Time Series Analysis and Machine Learning Methods,2020.0,,Hae Jung Chun,,1.0,
231363209ba68b9aa5c5a3003ecbe1db61296d71,10.24957/hsr.2018.26.4.53,Estimating the Real Estate Price Index Based on Sample House Price: Focusing on the Use of Machine Learning Method,2018.0,,성완 배; 정석 유,,1.0,
64fafde5ded40d609de1ae8b5ef20c0e42882b9c,10.2139/ssrn.3407231,Forecasting Australian Real House Price Index: A Comparison Study of Machine Learning and Time Series Methods,2019.0,Social Science Research Network,George Milunovich,,1.0,
8bdfaecc85eb64ed1feba397b68107ff52fef85f,10.22423/kreus.2020.12.2.63,Effects of Urban Railway attributes on Housing Prices: Case of Apartments in Incheon Metropolitan City,2020.0,,S. Bae,,1.0,
8b9d632fe59e5c3aa0ea9442aa831e15a6f50a4e,10.1007/s10901-020-09732-w,"Impacts of online shopping convenience and physical retail proximity on housing prices in Shenzhen, 2016–2018",2020.0,Journal of Housing and the Built Environment,Lu Shen; Yi-Ming He; Ling-Hin Li; K. Chau,,1.0,
fe9e4c0d765cfdc05ea37a8e8bcd80943b013b5b,10.1117/12.813170,Spatial statistics and GIS application study in spatial variability analysis of house prices: a case study of Dongguan,2009.0,GEOINFORMATICS,Zhi-Xiong Mei; Yang Ou; Wei-Ping Hu,,1.0,
7071ffd0881a6bf7b61ba98a7b4d297b2e31e118,10.59604/1046-2309.1004,Is Capital Expenditure Contagious? An Analysis of UCC Data from Ohio and Its Neighbors,2016.0,The Journal of Economics and Politics,P. Vanderhart; Zheng Zeng,"ECONOMIC CONDITIONS ARE OFTEN OBSERVED TO BE CORRELATED ACROSS SPACE AND TIME. ONE INTERPRETATION OF THIS PHENOMENON IS THAT ECONOMIC ACTIVITY IS “CONTAGIOUS.” THAT IS, GOOD OR BAD CONDITIONS IN ONE ECONOMIC AREA MAY LATER CAUSE SIMILAR CONDITIONS TO OCCUR IN NEARBY AREAS. THE PREVALENCE AND EXTENT OF THESE RELATIONSHIPS IS IMPORTANT TO UNDERSTAND FOR THOSE SEEKING TO FOSTER REGIONAL ECONOMIC DEVELOPMENT. WE FOCUS ON CAPITAL EQUIPMENT SPENDING AT THE STATE LEVEL AT A MONTHLY FREQUENCY. THIS IS POSSIBLE GIVEN OUR ACCESS TO A UNIQUE DATA SET, THE RANDALL-REILLEY CAPITAL INVESTMENT INDEX (RRCII). THIS INDEX MEASURES CAPITAL EXPENDITURE USING UNIFORM COMMERCIAL CODE (UCC) FORMS FILED EACH MONTH WITH EACH STATE’S SECRETARY OF STATE. THE DATA IS CLASSIFIED INTO THREE INDUSTRIES: AGRICULTURE, CONSTRUCTION, AND MACHINE TOOLS, AS WELL AS A COMPOSITE MEASURE. IN THIS STUDY, WE UTILIZE THE INDEX’S STATE-LEVEL DATA FOR OHIO AND ITS NEIGHBORS: MICHIGAN, INDIANA, KENTUCKY, WEST VIRGINIA, AND PENNSYLVANIA. OUR METHODOLOGY CONSISTS OF TYPICAL TIME SERIES TECHNIQUES: GRANGER CAUSALITY TESTS, VECTOR AUTOREGRESSIONS, AND THEIR ASSOCIATED IMPULSE RESPONSE FUNCTIONS. OUR INITIAL RESULTS SUGGEST THAT MI",0.0,https://collected.jcu.edu/cgi/viewcontent.cgi?article=1004&context=jep
ff68ce4812f51920af5137ea0ef8b1cec6e42261,10.52214/vib.v7i.8533,The Care Children Deserve,2021.0,Voices in Bioethics,G. Moorthy,"Photo by Chris Carzoli on Unsplash 
INTRODUCTION 
l.     The Need for a Children’s Hospital 
El Paso, Texas did not receive a children’s hospital until 2012, much later than would be expected given its demographics and geographic isolation. By that time, there were already nearly 250 children’s hospitals spread across the United States, some in areas far smaller, far older, and in far closer proximity to other urban centers.[1] Without accounting for its substantial population of undocumented immigrants,[2] El Paso is the country’s 22nd largest city (and situated in its 70th most populous county).[3] The nearest American city of comparable size is Phoenix, AZ, located about 350 miles away. Moreover, El Paso has a decidedly young demographic skew: more than 28 percent of the population is under the age of 18, compared to 26.5 percent of the population in Texas and 23.1 percent of the population nationally.[4] This gap is expected to widen in the coming years.[5] 
El Paso children also have less access to care than children in cities with comparable populations and population structures. Though the situation has improved in the last decade, El Paso contains several Health Professiona",0.0,https://journals.library.columbia.edu/index.php/bioethics/article/download/8533/4374
9a6d5a31cd4f57d50f7d67f27a9eaf55586e5c81,10.1111/ajph.12952,Australian Capital Territory January to June 2023,2023.0,Australian Journal of Politics &amp; History,Chris Monnox,"In early 2023, Canberra was the subject of two federal inquiries, one looking at the city's role as national capital and the other considering the ACT government's decision to compulsorily acquire Calvary, a local hospital. At the same time the Legislative Assembly focused on planning, an issue renowned for bringing out Canberrans' parochial side. In the event, however, the planning debate coincided with a national discussion about housing and development while the Calvary issue became entangled with the Territory's particular constitutional position. Urban planning in the ACT is highly contentious. It pits supporters of a denser Canberra linked by more public transport, among whom Chief Minister Andrew Barr is usually numbered, against those who prefer the low-rise suburbia that still characterises much of the city. It also inspires a good deal of grassroots organisation: many inner suburbs have active residents' groups, which usually oppose change, while Greater Canberra, a city-wide YIMBY (yes-in-my-back-yard) group founded in 2021, advocates for denser neighbourhoods and more permissive zoning, especially in these same suburbs. Little of this is unique to Canberra, and in 2023 ",0.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/ajph.12952
23b334178afeac4b04bc5a59299df12e06f88886,10.63544/ijss.v5i4.317,"The Role of Landscape Context, Management Intensity, and Vegetation Heterogeneity in Shaping Urban Park Multifunctionality – A Case Study of Chengdu",2026.0,Inverge Journal of Social Sciences,Kamran Ahmed; Muhammad Bilal; Mudassir Aziz; Saman Asif; Sohail Abbas,"Infrastructure Urban parks are very essential elements of green infrastructure, which provide various ecosystems services such as regulation of microclimates, sequestration of carbon, preservation of biodiversity, and recreation. But the size itself does not dictate the performance of parks. The paper examines the potential of the trio of linked aspects landscape context, management intensity, and vegetation heterogeneity to collectively influence urban park multifunctionality in Chengdu, a fast-urbanizing subtropical mega city. A stratified 3 x 3 x 3 sampling design was applied to categorize the parks by gradients of impervious cover surrounding maintenance intensity surrounding the park and vegetation diversity giving the 60 representative parks under 19 functional combinations. Multifunctionality has been measured based on the cooling intensity, storage of carbon, biodiversity, and recreational value. Spatial autocorrelations were studied with the help of the Morans I, and Ordinary Least Squares (OLS) and geographically weighted regression (GWR) were used to measure both global and local drivers. Findings show that vegetation heterogeneity is the most significant positive predic",0.0,https://invergejournals.com/index.php/ijss/article/download/317/554
48a2a9abc3f2a760faf64fbdddf34fb534b66302,10.15760/honors.1342,Analysis of Oregon Multi-Family Housing in Relation to COVID-19 Pandemic,2023.0,,Conor Paterson,"The purpose of this paper is to examine notable metrics of multi-family housing markets before and since the COVID-19 pandemic. The primary locations for the analysis of this issue were Portland, Oregon and other cities located near Portland. This paper attempts to demonstrate the ways in which the COVID-19 pandemic has affected the landscape of multi-family housing options and affordability since the year 2020. A literature review was first conducted to obtain a better understanding of what the discourse community surrounding housing changes has stated about the pandemic’s role in the housing market. After drawing conclusions from the prior literature, a data analysis was conducted on aggregate data from the year 2000 to the present in Portland, Oregon and other cities in the state. A descriptive statistical analysis was run to understand the data spread, then several t-tests were run on significant variables to determine if the time period of the pandemic had a significant statistical effect on said housing variables. The results of the statistical analysis corroborated the statements made in the literature review with regards to the pandemic’s impact on housing. By agreeing with",0.0,https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=2546&context=honorstheses
8dd1f1231f4fb7bef4fedc16b9c324762d079d5c,10.1200/jco.2026.44.16_suppl.1652,Are socio-economic status indicators barriers for enrollment in phase 1 clinical trials?,2026.0,Journal of Clinical Oncology,Reema Mody; Erica DeCecco; N. Florez; Vanisha Patel; Li-Li Zhang; A. Goy; T. Proverbs-Singh; Andrew L. Pecora; Nicole Lieber; Alison Morawaski; E. P. Mckiernan; Nicholas Hoffman; L. Sarmasti; Danielle Schillen; Nicole Montes; Michael H. Postma; Martin Gutierrez; Miguel González-Vélez,"<jats:p>
                    <jats:bold>1652</jats:bold>
                  </jats:p>
                  <jats:p>
                    <jats:bold>Background:</jats:bold>
                    Socio-economic Status (SES) indicators create barriers to access Phase 1 clinical trial (Ph1) enrollment, particularly for underrepresented minorities (URM). However, it remains unclear whether they directly affect enrollment after screening, or represent upstream barriers. We evaluated whether SES indicators impacted the enrollment rate in Ph1.
                    <jats:bold>Methods:</jats:bold>
                    All patients were screened for Ph1 at Hackensack University Medical Center (2014-2025). Patient ZIP codes were linked to the U.S. Census data (ACS 2018-2022) to derive neighborhood-level SES indicators: median household income (MHI), educational attainment (Bachelor), poverty rate (PR), unemployment rate (UR), and median home value (MHV). SES indicators were compared to national and state census benchmarks. The primary outcome was enrollment rate by SES indicators. We performed univariate analyses (Mann-Whitney U tests, chi-square), stratified analyses by race/ethnicity and gender, temp",0.0,
a04a58dc26af3431e4e3da3518c3c3b808c67b7d,,The Spatial Extent of the Effect of Foreclosures on Crime,2015.0,,Seth B. Payton,"Although neighborhood stability has long been considered a substantial determinant of crime, foreclosures have not been the subject of concerted research among criminologists until recently. A number of recent studies have examined the linkage between home foreclosures and crime. Though generally finding a significant relationship, studies have used different approaches and units of analysis. This variation led us to examine the spatial extent to which foreclosures affect a relatively small surrounding area. In this paper, we consider the spatial extent of the foreclosure effect on crime by estimating fixed effect negative binomial models using geocoded UCR data for 2003-2008 and foreclosure data to predict crime counts using the number of foreclosures within various small area radii. Results show that, independently and jointly, foreclosures are a predictor of crime up to at least a distance of 2,250 feet. Importantly, that effect declines with distance. We conclude with a discussion of the implications of those findings. 1.Introduction Scholarly interest in the external effects of foreclosures on neighborhood conditions has increased over the past decade. The general consensus is",0.0,
7e893d9c09c80aaac6989225c1212b70d906574e,10.2991/ssehr-16.2016.274,Carding on the western economic theory of Real estate price decision and Application,2016.0,,L.-J. Man,"In recent years, the problem of real estate price has become more and more concerned by the society and the government.,thus the composition of the real estate prices and the determining factors to become a topic of concern to the academic community. This article has carried on the back and context of the western theory of economic price, sorts out cost determination theory, utility price theory and equilibrium price theory in varying degrees of real estate price formation process has a different degree of influence and inspiration. After a brief review and the guiding of the theory in the real estate price application, and finally according to the theoretical analysis, the main supply and demand factors of Chinese real estate prices are proposed. Real estate has become a social focus more than ten years, the uncertain prices is not only a simple routine changes in commodity prices, but also affects the national economy and people's lives. People pays more attention to the price, the state implements a variety of macro-control means to intervene prices, a large number of wealthy gets more and more houses, but the ordinary is not able to undertake house, and in some cities people do",0.0,https://download.atlantis-press.com/article/25857778.pdf
0ed24bb36d42ae4122fdef341a99c7e05c43f170,10.5821/dissertation-2117-105137,Obtenció d’un indicador microterritorial de renda familiar disponible a partir de dades macroterritorials i contrastació empírica a nivell de secció censal : El cas de Terrassa,2016.0,,Josep Figueras,"Aquesta tesi té el seu origen en la tesi del Màster de l'any 2008, en què es desenvolupava mitjançant ecologia factorial un indicador socioeconòmic que corresponia al primer factor de l'anàlisi. Els treballs que tracten d'estudiar unitats petites, com la secció censal, es troben amb el problema important de la manca de dades oficials disponibles, especialment si es tracte d'informació de tipus econòmic. Les causes adduïdes per la no difusió són el secret estadístic, però també podria imputar-se a la manca de voluntat o interès de l'Administració. El resultat és que la manca de dades dificulta el subministrament d'informació necessària per a una gestió basada en la presa de decisions. La hipòtesi de treball i l'objectiu de la tesi, és que: ""És possible obtenir un indicador senzill equivalent a la Renda Familiar Disponible (RFD), fruit d'un model capaç d'oferir resultats acceptables en diferents àmbits territorials des de la província fins la secció censal. Així doncs, l'originalitat del treball es aquesta multifuncionalitat espacial ja que no existeixen precedents publicats d'aquestes característiques"" El pla de la tesi segueix tres parts: La primer tracta del marc teòric, que s'ha ",0.0,https://upcommons.upc.edu/bitstream/2117/105137/3/TJMEF3d3.pdf
534a286f8e2e99b1a12e40a7bb6297c41cf1b3e0,10.4148/2334-4415.1923,"Stephen Luis Vilaseca. Barcelonan Okupas: Squatter Power! Madison: Fairleigh Dickinson UP, 2013.",2017.0,,E. Bou,"Review of Stephen Luis Vilaseca. Barcelonan Okupas: Squatter Power!. Madison/Teaneck: Fairleigh Dickinson UP, 2013. This book review is available in Studies in 20th & 21st Century Literature: http://newprairiepress.org/sttcl/vol41/iss2/14 Stephen Luis Vilaseca. Barcelonan Okupas: Squatter Power! Madison: Fairleigh Dickinson UP, 2013. ix–xxix + 170 pp. This book deals with recent urban developments, namely the rise of a squatter movement in the city of Barcelona. In doing so, Stephen Luis Vilaseca offers welcome insights into the landscape of a variety of Spanish cities, while providing clues to recent changes in Spanish political and urban attitudes. Neither a sociologist nor a political historian, Vilaseca takes the right path when deciding to study the ways in which okupas (the Spanish neologism for squatters) are represented, by themselves and others, in a variety of media, ranging from blogs to film to poetry. Thus the main focus of the book is actually “reading.” Vilaseca reads representations of okupas, combining this with a study of the urban experience represented in the socialist Catalan imaginary in Barcelona by way of the squatter. He focuses on the reaction to this issu",0.0,https://newprairiepress.org/cgi/viewcontent.cgi?article=1923&context=sttcl
28db41af9f1b67282c43848a3cb4ef831cdf83ec,10.2991/icemct-15.2015.271,Discussion on Some Problems in the Development and Design of Medium and Small-Sized Housing,2015.0,,Jing-kun Fan,"A huge contracdition between housing construction and china's state situations, people’s housing needs has arisen. Some problems existing in the housing develop in china are analyzed, and it is verified that medium and small-sized housing will be a mainstream of housing develop. Discussion on the difficulties of medium and small-sized housing construction, existential problems and some mistake in medium and small-sized housing design was achieved. The discussion results have an important practical guidance significance for the development and design of medium and small-sized housing in the immediate future. Keywords-medium and small-sized housing; residential development; residential design; people-oriented. I. PROBLEMS EXISTING IN THE HOUSING DEVELOP IN CHINA A huge contradition between the housing construction and basic state situations, people's housing needs has arisen, many problems and contradictions are always discovered and it urges the housing problems of mediun or low-income stratum. Some main problems existing in the housing development are as followed. A. Outstanding Contradiction between Housing Supply and Demand With the acceleration of urbanization and an influx of i",0.0,https://download.atlantis-press.com/article/24365.pdf
62fba0b1ede9ea01b040185735e6bad32a23064a,10.15641/jarer.v6i2.1145,"Editorial for JARER Vol 6, Issue 2, 2021",2022.0,Journal of African Real Estate Research,Abel Olaleye,"Welcome to Volume 6 (2021), Issue 2 edition of the Journal of African Real Estate Research (JARER). JARER has provided valuable resources that support academics and professional researchers in Africa and those interested in the African continent. Our journal offers an exciting platform for disseminating scholarly and the different types of applied research engaged within the real estate sector in Africa. This issue is coming as the first regular issue of 2021. The journal's first issue gave way for the Special Issue published in June 2021. For this reason, we want to apologize to the authors whose papers were accepted earlier in the year for the delay in publication. To forestall this situation in the future, the JARER management team has decided to start publishing individual papers in digital format on our website once accepted and while waiting to be published with a full issue at the appointed time.   
Karen Gibler and Geci Karuri-Sebina's diligent and painstaking efforts in supporting the journal with the successful publication of the special issue titled: Women in African Real Estate and Urban Development Research are highly appreciated. Our gratitude also goes to the journal",0.0,https://doi.org/10.15641/jarer.v6i2.1145
f8cd79f7165ac74243582d9fde9ffc5d24772c6f,10.48264/vvsiev-20223103,Risk and returns in real estate development projects at the black swan test [Rendimento e rischio d’investimento immobiliare alla prova del cigno nero],2023.0,Valori e Valutazioni,P. Rosato; Raul Berto; C. D’Alpaos,"The real estate market is affected by great uncertainty due to the nexus of various factors: a) the specificity of the assets traded, which are illiquid, unique and very hetherogeneous from each other; b) the ‘structural disequilibrium’ of the market caused by the differences emerging in elasticity of supply with respect to demand; c) the non-competitiveness of the market, which often turns into a bilateral monopoly; d) the great variability of market prices. Since the subprime mortgage crisis that broke out at the end of 2006 in the United States, it has clearly emerged that, in a sector that represents about a third of world wealth, it is necessary, on the one hand, to implement proper and increasingly sophisticated valuation tools, to support the design of effective risk management strategies and, on the other hand, to improve the reliability of real estate data, in order to allow for a more robust verification of the hypotheses on the trend of the cash flows generated by the investment and a more accurate valuation of the investment risk and, consequently, of the project expected rate of return. The main objective of this work is to investigate the accuracy and robustness of th",0.0,https://siev.org/wp-content/uploads/2023/02/03_ROSATO-ET-AL-.pdf
4488f2767b0104a54d2f2d130ad2b1905ebe096f,10.32468/inf-pol-mont-eng.tr3-2026,Monetary Policy Report - July 2026,2026.0,,,"Banco de la República’s technical staff presented the July 2026 Monetary Policy Report, which examines key variables underpinning the macroeconomic forecast, recent inflation dynamics, economic performance, and the outlook for the coming months.
During the second quarter of 2026, inflation continued to rise, moving further from the 3% target, driven by higher labor costs, elevated household and public-sector spending, and higher food prices. At the same time, the Colombian economy recorded stronger growth, boosted primarily by consumption and investment in machinery and equipment. Inflation expectations remain high, and spending levels continue to exceed those the economy can sustain without generating additional inflationary pressures. During the remainder of 2026, prices are expected to rise before gradually moderating in 2027. In this environment, Banco de la República remains committed to taking the necessary measures to ensure the gradual convergence of inflation toward the 3% target.
Please review the Report
Inflation continued to rise during the second quarter, moving further away from the 3% target, driven by higher labor costs, robust consumption, and supply disruptions th",0.0,https://doi.org/10.32468/inf-pol-mont-eng.tr3-2026
45baf00f69a7c4fecd7894bc237e20ad5e93e9da,10.15396/eres2019_97,The Impact of the Heathrow Northwest Runway Announcement on Residential Property Prices in Greater London,2019.0,26th Annual European Real Estate Society Conference,L. Papageorgiou,"As prior research has shown, airport expansions in densely populated urban areas affect homeowners in surrounding areas. The effect on prices is expected to be particularly pronounced in the London urban area due to the high absolute valuations. This study will examine the announcement effects of (a) the release of the planning report for Heathrow’s Northwest Runway on 01.07.15, (b) the acceptance of the plan by the Secretary for Transport on 14.12.2015, and (c) the official government approval for the initiation of Heathrow’s airport expansion on 25.10.2016. The research focus of this paper lies on the extent to which prices of single family, terraced and semi-detached homes in those areas not yet affected by Heathrow’s runway noise changed as a consequence of the announcements. In this context, it is of particular interest whether the observed changes in property prices match the noise contour proposed by the official planners’ report. The novelty of this study lies in the fact that we focus on the home price effects of the announcement of an airport expansion. For airports as opposed to other infrastructure projects, announcement effects have so far not been analyzed in the lite",0.0,
b3bf74587270c251f487dd013e05506b54fddacc,10.38007/proceedings.0000491,Linyi City Real Estate Market Development Problems and Countermeasures,2020.0,2020 Conference on Economics and Management (CEAM2020),Wen-Jun Li,"The residence is the basic living material and must consumer goods, the development of the housing market is closely related to people's life and the commodity house is the important component of the residential market. Therefore, the healthy development of the housing market is related to the national economy and social development. This paper will analyze and study the current situation of the commercial housing market in Linyi city, and put forward relevant countermeasures to some problems existing in the development of commercial housing in Linyi city. This topic first from the real estate industry and the related concepts of commodity housing and real estate market operation characteristics, the related concept, and then analysis of Linyi commodity residential market development present situation, summarizes the current Linyi city commodity housing market problems, and puts forward relevant strategy, for the consumer 、 business and government decision-making to provide reference and experience. Foreword As early as 1990-2004, foreign research on real estate industry has been basically mature. In this period, papers published in Journal of real estate finance and economics and ",0.0,
3fd3b8923ac39e73951c830825be153e924f9d3b,10.2991/ssemse-15.2015.288,Research on Metaphor and its Functions in Economic Text,2015.0,,X.-L. Liu,"This study aims to explore metaphor models and analyze their important functions in economic text. The corpora are from the articles of The Economist and China Economic Times from 2011—2012. Metaphorical expressions are selected and classified. It is found that metaphors are pervasive in economic texts. Three functions of metaphor, namely rhetoric, cognitive and textual functions, are discussed. This research is significant in helping readers to build or strengthen metaphorical thinking for reading and writing, so as to better promote economic exchanges between China and western countries. KEYWORD: Metaphor; Function; Economic text International Conference on Social Science, Education Management and Sports Education (SSEMSE 2015) © 2015. The authors Published by Atlantis Press 1124 3.2 Main metaphor models According to the result, a great number of metaphors exist in economic text. They are in variety. From further analysis, it is found that all of these metaphors are rooted in people’s life and social experiences. Some metaphors have already become conventional ones, like MONEY IS LIQUID, ECONOMY IS BUBBLE, and MARKET IS CONTAINER, which are commonly accepted by people. Four main ",0.0,https://download.atlantis-press.com/article/25842265.pdf
cef76f0068eb0c38d4e099a6478b8ff6a8b06339,10.1108/ijhma-05-2026-0152,Urban infrastructure and its role in shaping Moscow’s residential real estate market,2026.0,International Journal of Housing Markets and Analysis,E. Semerikova; Anh Kiet Tran,"
 
 This paper analyzes the apartment prices in Moscow using geographically weighted regression (GWR) to account for the spatial non-stationarity and local variations in the pricing of Moscow’s heterogeneous real estate market on the sample of 3,504 flats. The results suggest that the impact of infrastructure on property values varies considerably across neighborhoods, which is indicative of underlying socioeconomic trends and urban development patterns which are not captured by traditional global ordinary least squares (OLS) models. By empirically updating and extending the largely city-wide, transaction-era literature on Moscow housing with a post-pandemic, spatially disaggregated data set, and by contrasting global and locally varying estimates, this paper contributes to the urban economics literature on spatial heterogeneity in housing markets and offers policy-relevant, location-specific evidence for stakeholders engaged in urban development and residents’ investment decisions.
 
 
 
 The empirical strategy involves a comparative analysis. First, an OLS model is established to provide a baseline understanding of the average city-wide effects. Subsequently, the GWR model is imp",0.0,
5089f9bd568c68019b7f90ef3bdc4323125e700d,10.32996/fcsai.2024.3.2.10,From Utility Bills to Retrofit Finance: An AI Framework for Energy-Burden-Aware Underwriting in Residential Decarbonization,2024.0,Frontiers in Computer Science and Artificial Intelligence,Shah Farhan Rabbani; Yusuf Oli Rahat; Md Kamrul Islam,"This study develops a literature-driven conceptual framework for energy-burden-aware underwriting within the context of U.S. residential decarbonization finance. It addresses a critical and growing disconnect between the policy-driven urgency to decarbonize residential buildings and the financial constraints faced by households most in need of retrofit investments. Notably, those households that stand to benefit the most from energy-efficiency improvements are often underserved by traditional underwriting models. Conventional lending frameworks prioritize stable income streams, established credit histories, and property-based collateral signals, whereas energy-burdened households—characterized by high utility costs, inefficient housing conditions, and constrained liquidity—exhibit affordability dynamics that are poorly captured by these models but are highly relevant for socially beneficial investment. Drawing upon interdisciplinary literature spanning energy burden, building decarbonization, alternative data underwriting, explainable artificial intelligence, and algorithmic accountability, this paper proposes an AI-enabled underwriting framework that integrates utility-bill histor",0.0,https://al-kindipublisher.com/index.php/fcsai/article/download/12374/11063
b0d51fe65141e370661902af948c04e62edff65e,10.56201/ijefm.v11.no9.2026.pg96.129,"Cost-Push Inflation, Inelastic Demand, and Non-Traditional Buyers: A Three-Dimensional Analysis of Nigeria's Urban Real Estate Resilience Despite Macroeconomic Decline",2026.0,International Journal of Economics and Financial Management,V. Ihezukwu,"Nigeria’s urban housing market presents a paradox between 2010 and 2025: despite severe 
macroeconomic decline marked by volatile GDP growth, average inflation of 15.7%, exchange 
rate depreciation, and a shrinking middle class from 23% to 11% of the population, prime 
residential property prices in Lagos, Abuja, and Port Harcourt rose over 380% while luxury estate 
construction continued. This study investigates the three-dimensional drivers of this resilience by 
integrating cost-push inflation, demand inelasticity, and the emergence of non-traditional buyers. 
The broad objective is to decompose the relative contributions of cost-push and demand-pull 
factors to housing inflation, estimate price and income elasticity of demand across housing 
segments, assess the impact of diaspora remittances, High Net-Worth Individuals, politically 
exposed persons, and illicit-wealth actors on demand and prices, and explain developer supply 
response despite weak middle-class purchasing power. Anchored on Keynes’ (1936) inflation 
typology, Marshall’s (1890) and Engel’s (1857) elasticity theories, Acemoglu and Robinson’s 
(2012) political economy framework, and Aalbers’ (2016) financializatio",0.0,"https://iiardjournals.org/get/IJEFM/VOL. 11 NO. 9 2026/Cost-Push Inflation, Inelastic Demand, 96-129.pdf"
f72a9b8f9152597cdaab7aaee67fefacff01ceb1,10.24940/theijbm/2019/v7/i1/bm1812-001,"Effect of Economic Factors on Mortgage Uptake in Housing Finance Corporation in Eldoret Town, Kenya",2019.0,International journal of business management,Jeremiah Koskei; J. Munyua; F. Odoyo,"payments, mortgage payments, interest on loans and stamp duty. The Government of Singapore played a major role by offering incentives to make the product attractive its citizens and also offering a strong institutional support (Chia and Tsui, 2015). In comparison to Japan, UK, France, Singapore has the highest ratio of household residential property. The recent statistics from the department of statistics of Singapore indicates that the house ownership stood at 88.6% (Government of Singapore, 2011). This leaves Singapore as one of the countries with the highest house ownership among its citizens. In the United Kingdom (UK) pension mortgage is an interest only mortgage with an additional investment plan in the form of a personal pension. This implies that members of schemes utilizing this facility pay off the interest component every month, and rely on a portion of the remaining accumulated benefits at the point of retirement to pay off the capital Abstract The purpose of the study was to investigate the effect of economic factors on mortgage uptake in Eldoret Kenya. Though the Kenya government through the constitution has recognized the right to accessible and adequate housing with",0.0,http://www.internationaljournalcorner.com/index.php/theijbm/article/download/148295/103797
d624366d50eac24b3a81ded5bd4ff4c3da58b90b,10.1108/jrf-09-2025-0422,Investigating the dynamics of house prices: an asymmetric analysis of macroeconomic determinants,2026.0,The Journal of Risk Finance,Petros Gkiosis; X. Chapsa,"
 
 This study investigates whether real house prices in Greece respond asymmetrically to key macroeconomic fundamentals and whether such asymmetries are consistent with downward price rigidity. Motivated by the persistent rise in property prices and its implications for affordability and macro-financial stability, we examine differential effects across expansionary versus contractionary phases and assess the role of major structural disruptions.
 
 
 
 We model real house prices in Greece as the dependent variable and apply the nonlinear autoregressive distributed lag (NARDL) methodology, decomposing GDP per capita, inflation (CPI) and mortgage interest rates into positive and negative partial sums to estimate asymmetric short- and long-run effects. Regime shifts are explicitly accounted for through an endogenously identified structural break at 2012Q2, aligned with the March 2012 PSI, strengthening inference on cointegration and long-run relationships over an extended, crisis-prone sample.
 
 
 
 The results confirm cointegration with a significant speed of adjustment and pronounced asymmetries in the transmission of shocks to real house prices. In the long run, lagged GDP per ca",0.0,https://doi.org/10.1108/jrf-09-2025-0422
42c9a7e7f4e719f905e935227bc6986283657fea,10.21837/pmjournal.v15.i2.349,INTRODUCTION TO SPECIAL ISSUE 2 : 2017,2017.0,,D. Omar; O. L. H. Leh,"The urban planning system seeks to guide appropriate development to the right place and to prevent inappropriate development from taking place aiming at securing sustainable development. However, rapid urban growth due to high fertility and rural-urban migration have exerted pressure on the environment of urban settlements. Urban settlements in developing countries, especially, are facing increasing issues and challenges in various aspects, i.e. physical, social and economic. In Malaysia, various researches and studies have been carried out to understand the issues and challenges of urban settlements in the country. This issue of Planning Malaysia aims to share findings of researches/studies in various aspects of urban settlement and planning in Malaysia. It covers the aspects of globalization and urban planning, urban forms, finance, housing, social, agro-tourism, recreation, urban spaces, transportation, and heritage. The authors of paper 1 suggest that neoliberal globalization certainly gives rise for enabling urban settlements as can be seen in the area of Kuala Lumpur and Selangor. However, the process brings further challenges to the Malaysian planning system such as social i",0.0,https://doi.org/10.21837/pmjournal.v15.i2.349
2af08113f7a8d5fe56293541696538cce5f7e1ab,10.48175/ijarsct-23243,House Price Prediction,2025.0,"International Journal of Advanced Research in Science, Communication and Technology",Nargis A. Mulla; Harshit Nandkumar Gorad; Sarthak Balaso Patole; Pradnya Adhikrav Gorad; Shweta Jadhav,"House price prediction is a crucial aspect of real estate analytics, impacting buyers, sellers, investors, and policymakers. The real estate industry is a dynamic sector where property values fluctuate based on numerous factors such as economic conditions, location, infrastructure, demand-supply balance, and regulatory policies. Traditional valuation methods often rely on expert opinions and manual assessments, which may introduce biases and inconsistencies. In contrast, data-driven approaches, particularly machine learning (ML) techniques, offer a more accurate and systematic way to estimate property values.
This study explores the effectiveness of machine learning models in predicting house prices by analyzing historical real estate data. Various predictive models, including linear regression, decision trees, random forests, gradient boosting machines (XGBoost), and deep learning algorithms, are evaluated in terms of their performance. The research aims to determine which model provides the highest accuracy while ensuring interpretability and efficiency.
Data pre-processing plays a vital role in enhancing the accuracy of house price predictions. The dataset used in this study inc",0.0,https://doi.org/10.48175/ijarsct-23243
e8dc0e684d1cf0ef4b9a9d75b8804bb69e58405e,10.37043/jura.2013.5.1.4,"SMART GROWTH AND THE CHALLENGE OF NIMBY: MULTIFAMILY DWELLINGS AND THEIR ASSOCIATION WITH SINGLE-FAMILY HOUSE SELLING PRICES IN TALLAHASSEE, FLORIDA, USA",2020.0,,H. Gibson; M. Becker,"IntroductionHomeownership is the single largest financial investment most people will make during their lifetime (United States Department of Housing and Urban Development 1995). Furthermore, housing choice can have implications for quality of life, so housing consumers make purchasing decisions based on many factors, including affordability, location, specific housing or property attributes, and/or the characteristics of the surrounding neighborhood (Tiebout 1956, Fischel 2000). Because external neighborhood qualities are part of the decision to buy a house, it can also influence the monetary value of housing. Thus, neighborhood activities are important to many homeowners. Accordingly, NIMBY (""not in my backyard"") activism is frequent amongst homeowners. NIMBY citizens will participate in local government to protest development in their neighborhoods when they consider the development undesirable. One potential type of neighborhood development often perceived as undesirable by single-family homeowners in the United States (US) is multifamily housing. US single-family homeowners will regularly protest proposed multifamily construction when it is close to their house because they fe",0.0,http://www.jurareview.ro/resources/pdf/volume_11_smart_growth_and_the_challenge_of_nimby:_multifamily_dwellings_and_their_association_with_single-family_house_selling_prices_in_tallahassee_florida_usa_abstract.pdf
8429e05c723295a3940e231e8ebc76a90a8c447a,10.26686/wgtn.17135018,"Essays on modern economic issues in international trade, exchange rates and housing",2019.0,,Karam Shaar,"The common theme of the three papers in this thesis is the focus on the impact of data choices on empirical research in Economics. Such choices can be about the source of data; should we source the data from country A or country B in a bilateral trade relation? Is there a way to reconcile the discrepancies in international trade data? In investigating the impact of exchange rate on trade, should we choose high-frequency or low-frequency data? What does the choice of a certain frequency imply for the econometric analysis? In assessing the impact of housing wealth on household consumption, what are the benefits of choosing household-level data? How can we take advantage of aggregate data on house prices to circumvent the endogeneity arising from household-specific confounding factors? This thesis shows that data choices can strongly affect our conclusions regarding several modern economic issues.  The first paper is titled ‘Reconciling International Trade Data.’ International trade data are filled with discrepancies–where two countries report different values of trade with each other. We develop an index for ranking countries’ data quality based on the following notion: the more a co",0.0,https://openaccess.wgtn.ac.nz/articles/thesis/Essays_on_modern_economic_issues_in_international_trade_exchange_rates_and_housing/17135018/1/files/31686344.pdf
98eb7f096d1a2289ccb58679d1e006bb0bce7ebe,10.20429/amtp.2011.01,Tales of Two Parks: The Promotion Efforts to Promote Healthy Lifestyles Using Community Open Spaces,2011.0,Association of Marketing Theory and Practice Proceedings,Renée J. Fontenot; Rachel Smith,"In recent years, communities across the country have realized that urban growth must incorporate open spaces to meet the expectations of a ""livable"" community. In addition, parks and open spaces help the local economy through higher property values which in turn helps local governments with increased property tax values (Robert Wood Johnson Foundation 2010). In determining the rankings for best places to live, retire and work – “quality of life” makes up a large portion of the formula (Taylor 2008) and always includes the built and natural environment and how these two have been effectively woven together to make the ideal community. Quality of living is always important in studies of the ""Best"" and ""Worst"" places to live, work, raise kids, that are often printed in publications such as Fast Forward, Inc. and Money magazine (CNNMoney.com 2008). Being on a list of the ""Best Places"" brings positive exposure and interest to a community.
Along with quality of living, increasingly, studies report on the overall healthiness of its citizens, measured by various elements but often dependent upon the availability of sports and recreational activities (Mercer LLC 2010). The availability of r",0.0,
3398615210a40ff309b2ba515689202ec325d6a4,10.1108/ijhma-02-2026-0047,Housing price dynamics and macroeconomic anchoring under prolonged inflation: evidence from Türkiye,2026.0,International Journal of Housing Markets and Analysis,Nur Aydın; Y. Demir,"
 
 This study examines the determinants of housing prices in Türkiye, a high-inflation emerging economy where housing price growth outpaces consumer inflation. It assesses whether housing prices remain aligned with macroeconomic fundamentals or whether financial and structural factors increasingly drive price dynamics. This analysis aims to determine whether short-term financial volatility and policy changes have overridden long-term economic drivers in shaping housing markets.
 
 
 
 The analysis uses monthly data for Türkiye covering the period from 2013:M01 to 2025:M11. Separate demand-side and supply-side models are estimated within a Fourier-based framework. Long-run relationships are examined using Fourier unit root and cointegration tests, which allow for gradual structural shifts in the series. The study looks at short-term interactions using Toda-Yamamoto causality tests. The key variables in the analysis include housing credit volume, housing loan interest rates, consumer price inflation, cement production and construction costs. Together, these factors help capture the financial conditions and production-related pressures that drive changes in housing prices.
 
 
 
 No ",0.0,
80106dffb0df99aad015ebc8d189ec22ea60179d,10.21273/hortsci.36.7.1334,Price Elasticities of Demand for Mums and Pansies Sold in Independent Garden Centers,2001.0,,M. Wohlgenant; C. Safley; A. Rezitis,"Data from a survey of North Carolina independent garden center customers in Fall 1996 were used to determine the price responsiveness of mums and pansies. A survey was conducted of four garden centers in the Raleigh, N.C., area and four garden centers operating in the Triad marketing area (Greensboro, Winston-Salem, and High Point, N.C.). Information collected on 1608 consumers included various socioeconomic and demographic variables (age, value of residence, type of residence, number of years in the residence, housing tenure, and employment status) as well as plant purchase information (plant price, plant types, and plant sizes). Price responsiveness of consumers was estimated by analyzing how customers' responses change as prices varied from one store to another and from one location to another. Measures of price responsiveness indicated statistically significant price elasticities of demand of -0.76 for mums and -0.80 for pansies. These elasticities can be used to indicate how industry sales would respond to a change to the industry that affects all firms in the same way—such as the response to an increase in energy costs. The paper shows how to use the elasticities to develop p",0.0,https://journals.ashs.org/downloadpdf/journals/hortsci/36/7/article-p1334.pdf
3f9d3656180e3ede316217ed7730fe9471310f30,10.2991/emcm-16.2017.206,Measuring the Spatial and Temporal Diffusion of Urban House Prices---Taking the Case of 18 Cities in East China,2017.0,,He Wang; Chen Ma,"Based on spatial vector auto-regression model, this paper analyses the spatial and temporal diffusion of urban house prices in East China and examines the impact of price fluctuation of Shanghai and Hangzhou. The results show that assuming Shanghai as the price diffusion center city, more than half cities are affected significantly by the neighbor lag. Current price changes in Shanghai and Hangzhou have the biggest influence scope to other cities, while the house price in Hangzhou is the most difficult to be affected in the long-run. Compared with Shanghai, Hangzhou has greater and longer impact on other cities. It is concluded that the spatial and temporal diffusion exists in East China. Transportation accessibility, economic ties, information dissemination could explain house price diffusion in East China. Introduction With the acceleration of urbanization in China, the economic and social transactions between the big and medium cities have strengthened a lot, accompanied by the closer interactions between the real estate markets. East China, being as one of the fastest growing areas in China, its real estate market is always considered with great investment potential. Thus, whet",0.0,https://download.atlantis-press.com/article/25870586.pdf
87b935592dd33aa720d3f9393c65f2e1d98d4b40,10.20431/2454-8685.0702001,"Road Connectivity and Proximity of Fringe of Raipur City, Chhattisgarh",2021.0,International Journal of Research in Geography,S. Mukherjee,"Roads play a significant role in the development of a town, city, region and a country, as they affect accessibility and the relative attractiveness of all locations (Gairhe, 2013). The accessibility of major roads provides relative advantages, where commercial users enjoy the opportunities created by the investment for rural road construction. Thus increase in demand for transport services and a corresponding decrease in the vehicle operating cost will be related with the improvement of road conditions (Walle, 2007). Rural road is most essential to facilitate market entry of small holders from village to town (World Bank, 2006). Female employment and off-farm opportunities are significantly increased by road intervention (Lakshin and Yemtsov, 2005; Walle, 2007). Road accessibility has profound impact on property values; and, properties located at the point where two or more roads meet command greater value than those located at the nodal points (Omoogun, 2006). Road transport considered as the only link to connect the rural and under developed areas with developed areas (Khan and Dehre, 1998). Maintenance of roads is essential for better and faster services, and minimum transport ",0.0,https://doi.org/10.20431/2454-8685.0702001
876eae43cf96a7dfa471d309316629469062911d,10.65227/jaia.v1i1.5,PREDIKSI HARGA REAL ESTATE MENGGUNAKAN METODE REGRESI LINEAR BERBASIS MACHINE LEARNING,2025.0,Journal of Artificial Intelligence Application,Nanda Khalijah Zainal,"Abstrak: Kenaikan dan penurunan harga real estate yang cepat dan tidak stabil dapat berdampak pada pertumbuhan pendapatan masyarakat, akses perumahan, dan menciptakan ketidaksetaraan pembangunan antar wilayah. Diperlukan upaya prediksi untuk meminimalisir fluktuasi harga real estate yang tidak stabil. Kebijakan regulasi, seperti pembatasan spekulasi dan pemantauan ketat terhadap peminjaman berisiko, diperlukan untuk menjaga stabilitas pasar. Prediksi menggunakan metode regresi dimana dalam metode regresi terdapat 7 metode lagi meliputi (1) Linear Regression, (2) Support Vector Regression – Linear, (3) Support Vector Regression – RBF, (4) Decision Tree Regression, (5) Random Forest Regressor, (6) Gradient Boosting Regression, (7) NLP Regressor yang diterapkan dalam penelitian ini. Berdasarkan 7 metode regresi tersebut akan dicari 1 metode terbaik dengan nilai akurasi paling terbaik yang akan digunakan dalam proses pengolahan deploy untuk memprediksi harga rumah per unitnya. Hasilnya metode MLP-Regressor keluar sebagai metode terbaik karena metode tersebut paling banyak menghasilkan nilai akurasi terbaik pada saat pengujian akurasi dengan menggunakan 4 pengujian akurasi dengan rasio ",0.0,https://jurnal.mutiaraamaliyah.com/index.php/jaia/article/download/5/9
c64bf80c31e7eb4ac81783c13593b181b78cb490,10.54220/6449.2024.32.92.012,"Trends in the Housing mortgage lending market: genesis, modernity",2024.0,Учет и статистика,Инна Анатольевна Полякова; Тамара Макаренко; Г. В. Павленко,"Введение. В статье в рамках системно-эволюционного подхода осуществлен аналитический обзор воздействия макроэкономической нестабильности на процессы, складывающиеся на рынке кредитования, в частности ипотечного жилищного кредитования, в контексте их влияния на уровень жизни населения. Представлен аналитический обзор генезиса кризисов в сфере недвижимости в странах мира и в России, и их влияние на развитие экономических процессов. Осуществлен анализ доходов, уровня занятости и уровня бедности населения России как важнейших детерминант сферы кредитования. Представлены современные процессы на рынке ипотечного кредитования России и регионов ЮФО. Материалы и методы. Информационно-аналитический обзор в контексте исторической ретроспективы степени воздействия макроэкономической нестабильности на процессы, складывающиеся на рынке кредитования, в частности ипотечного жилищного кредитования, в контексте их влияния на уровень жизни населения. Результаты исследования. Складывающиеся траектории числа продаж и динамика цен на рынке ипотечного жилищного кредитования во многом обусловлены размерами реальных доходов населения, состоянием рынка труда, в частности уровнем занятости, уровнем бедности,",0.0,
6e6af0215c423a57c5d41c8628d71feb9be7c10c,10.1080/16078055.2022.2029209,Editorial,2022.0,World Leisure Journal,Atara Sivan,"In the first issue of 2022, we have five research papers by authors from Australia, Spain, the U.S., and Czech Republic, on topics of playspace, youth leisure, extracurricular activities, parks, and drumming as leisure activity. The issue also includes four book notes and a piece of news from the World Leisure Organization Secretariat. In the first research paper, Rosemary Black and Janice Ollerton explored the extent to which a playspace in Australia, designed to encourage social inclusion through play, was meeting its goal. Following an examination of children’s right to play and the key legislation and policies related to playspace provision in Australia, the researchers conducted their study drawing on the Australia’s early childhood learning framework of Belonging, Being and Becoming. Results indicated that the playspace promoted social inclusion as manifested in users’ feeling of safety and belonging and in the interactions experienced at the place. The second research paper reports on a study by Idurre Lazcano, Aurora Madariaga, Sheila Romero, and Douglas Kleiber on school students in Northern Spain. The study focuses on the impact of young people’s self-management of peer g",0.0,https://www.tandfonline.com/doi/pdf/10.1080/16078055.2022.2029209?needAccess=true