Property Prices Research Articles

THE IMPACT OF SHARING ECONOMY (AIRBNB) ON HIGH-RISE NEIGHBOURHOODS BASED ON STRA GUIDELINES: URBAN PLANNERS’ PERSPECTIVE

This study examines the impact of Airbnb as part of the sharing economy, which has introduced various challenges in high-rise neighbourhoods. It explores how the implementation of Short-Term Residential Accommodation (STRA) guidelines can help regulate short-term rental activities and address emerging issues in high-rise residential areas. Specifically, the research aims to explore the impact of Airbnb from the perspective of urban planners, focussing on how this service affects neighbourhoods and cities. Using a qualitative approach, semi-structured interviews were conducted with key stakeholders, and the findings were analysed through thematic analysis. The results reveal both positive and negative implications of Airbnb. On the positive side, Airbnb stimulates the state's economy, particularly through the tourism sector, and provides an alternative source of income for residents. On the downside, the presence of short-term rentals can lead to increased property prices and raise concerns about neighbourhood safety. This research suggests that the STRA guidelines could play a key role in creating a balanced solution for the future of short-term rentals in Malaysia, ensuring that both residents and businesses can benefit from this evolving industry.

Smart Renting: Harnessing Urban Data with Statistical and Machine Learning Methods for Predicting Property Rental Prices from a Tenant’s Perspective

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 potential of ML algorithms in accurately predicting rental house prices. Moreover, it envisions the practical application of this research with the development of a user-friendly website. This platform could revolutionize the renting experience, empowering both tenants and real estate agencies with the ability to estimate rental values based on specific property attributes and have access to its statistics.

Valuation and Volatility of Virtual Properties in Metaverse: Exploring New Market Opportunities and Speculative Dynamics Based on Decentraland

Existing reality has been repeatedly subjected to multifaceted and multidimensional analyses. However, alongside it, a virtual reality exists, populated with virtual art, objects in games, cyber-pets, and virtual properties, collectively worth billions of real dollars. This virtual reality is associated with the concept of the 'metaverse' (a term derived from 'meta' and 'universe'), signifying a space that facilitates life in a virtual world. This space has a quantifiable value and is traded on specially designed platforms. The study focuses on the largest of these platforms, Decentraland, where users can purchase virtual properties using a cryptocurrency called MANA. Based on an analysis of 207 property transactions, the study examines whether these properties can be appraised using typical approaches applicable to “real” properties. It explores the relationship between spatial attributes, property price, and price volatility over time, calculating correlation coefficients for this purpose. Additionally, the study investigates the number of property sales, including multiple sales, and the proportion of properties with ITEMs on them, determining the correlation coefficients between price and parcel attributes. This study is the first to accomplish what researchers in the real estate market have long been doing. The results distinctly show that the virtual real estate market is governed by different factors compared to the traditional market, with property price and location remaining as two crucial aspects in the decision-making process for purchasing a property.

Residential Marginalization of Renters: Social Adaptation of Urban Youth in Suburbanized Living in First-Tier Cities

This paper explores the phenomenon of residential marginalization in China’s first-tier cities, focusing on its impacts on urban youth and their adaptive strategies in suburbanized living. As Beijing, Shanghai, Guangzhou, and Shenzhen transform into global economic hubs, the escalating housing crisis—characterized by rising property prices, speculative markets, restrictive urban planning, and the exclusionary Hukou system—has displaced low- and middle-income renters, particularly young professionals, to suburban peripheries. This spatial exclusion exacerbates social inequality, limits access to urban resources, and undermines the cultural and economic vitality of these cities. Drawing on the intersection of housing market dynamics, urban planning policies, and socio-economic barriers, this paper provides an in-depth analysis of the systemic drivers of marginalization. It examines the resilience and ingenuity of suburbanized youth, highlighting their use of technology, co-living models, grassroots initiatives, and entrepreneurship to navigate the challenges of suburban living. By reimagining suburban spaces through innovation and community-building, young renters demonstrate remarkable agency, though their adaptations also underscore the pressing need for structural reforms. The paper concludes by advocating for equitable housing policies, inclusive urban planning, and sustainable suburban development to address the root causes of marginalization and ensure first-tier cities remain accessible, dynamic, and inclusive spaces for all residents.

Green Buildings and Sustainability: A Review of Economic and Environmental Impacts

A key tactic in combating climate change, energy efficiency, and sustainable resource use is the use of green buildings. Green buildings, which are distinguished by increased water and energy efficiency, less environmental impact, and higher occupant comfort and health, provide substantial advantages at both the individual and metropolitan levels. These include greater transaction and occupancy rates, reduced operating expenses, higher property prices, and wider societal benefits including market expansion and employment creation. Despite these advantages, green buildings are still not widely adopted, especially in the residential sector. Research has emphasized the significance of comprehending consumer behavior, showing that, regardless of cultural or geographical variations, economic, environmental, and socio psychological aspects have a significant impact on the intention to invest in residential green buildings. Additionally, it has been demonstrated that sustainability characteristics in real estate development have a major impact on property values, which is why industry participants and governments must include these elements in their strategy. Evidence also points to a complicated dynamic in commercial real estate markets, where the rise of green buildings may have an impact on property values and rental rates. This analysis highlights the economic and environmental dynamics of green buildings by combining the results of other studies, and it offers a basis for promoting sustainable real estate development and policy-making globally.

Cointegration and Economic Dynamics: Unraveling the Interplay between Gold Prices and Residential Property Prices in Five Different Nations

This abstract delves into the intricate relationship between gold prices and residential property prices in China, Singapore, India, Iran, and Russia. Using a comprehensive dataset, our analysis elucidates the nuanced causal dynamics among these two economic variables. The findings reveal diverse interactions shaped by the interplay of local and global economic forces. In China, a significant decline in property values and a slight drop in gold prices highlight distinct market characteristics. Conversely, Singapore exhibits positive divergence, with an increase in property prices and substantial growth in GPs. India presents a contrasting pattern with a modest uptick in property values but an alarming decline in gold prices, thus suggesting a unique economic dynamic. Robust causal relationships are identified in the cases of Iran and Russia which underscore the significant influence of specific factors on the interplay between property value and gold prices. This foundational investigation emphasizes the necessity of tailored, context specific analyses and underscores the fluid nature of the relationship between the real estate and precious metal markets. The study provides valuable insights for economic forecasting and policy decisions, thus paving the way for further explorations into the intricate relationships that shape our global economic landscape.

Machine learning-based forecasts of residential property prices in Hangzhou City, Zhejiang Province, China

Machine learning-based forecasts of residential property prices in Hangzhou City, Zhejiang Province, China

Въздействие на териториите за далекоперспективно развитие върхулокалните имотни пазари

The article explores the potential of long-term development zones in providing new land for construction projects in areas with high demand, as well as the impact of lifting restrictions on the development of these territories on the real estate market. Bulgarian and foreign publications on the topic are reviewed. An analysis is conducted on several key indicators related to the demand, supply, and prices of properties in the studied locations. A market study is performed on specific examples of long-term development zones and the areas surrounding them. Conclusions are drawn about the potential of these zones to meet the growing demand for construction land, as well as the positive and negative effects of urban planning on the market, investors, and society. A forecast is made regarding the state of two local real estate markets aroundlong-term development zones, which are expected to be "released" for development after 2025.

Application of robust estimation methods in real estate valuation

Motives: Conducting real estate appraisals in well-developed markets presents a multitude of data analysis challenges. Some property price data may contain outliers that can significantly affect the valuation process and, as a result, the estimated value. In the case of real estate valuation regression models, estimation is most often based on the least squares method, where outliers are taken into account just like the rest of the data. Eliminating or minimizing the influence of outliers can lead to more reliable estimation results. Such problems can be solved by implementing robust regression methods. Aim: The main goal is to determine whether robust estimation methods using M-estimators with multiple regression models can provide more accurate estimates of property values. Results: Research has shown that multiple regression models using robust regression methods can be applied to estimate property values. The use of different types of M-estimators allows for the objective elimination of outliers through algorithms that operate on the entire data set. The calculations are carried out iteratively, and at each iteration step the residuals are verified and the observations are re-weighted. The following M-estimators were considered: Huber, Hampel, Tukey, Faire, Cauchy and Welsch. The reference point was the estimation results from the ordinary least squares method (OLS). All analysed M-estimators led to an increase in the coefficient of determination value and a decrease in standard estimation errors. Each algorithm detected the outliers. The valuation results for the selected properties were also more reliable. The results obtained depend on the characteristics of the data, and the choice of the best estimator may vary across different property markets. The selection of the best estimator may even vary within the same local property market, where the valuer makes subjective assessments of the location or other attributes.

Podstawy i zasady oszacowania nieruchomości dla potrzeb egzekucyjnych

The article presents methodical assumptions and legal regulations that provide for the estimation of the value of real properties for the purpose of enforcement proceedings conducted by bailiffs. It discusses differences in the interpretation of the market value and price of real properties and identifies reasons for price deviations from the market value. The article proves that to estimate the market value, the knowledge of legal regulations and the methodical basis is insufficient and it is also necessary to know the market segment where the real property to be evaluate is located, as well as the way of conduct of market players. The article also discusses rules to be followed by property experts during the estimation of the value of real properties, which are necessary to guarantee fair and efficient debt collection from such real properties by bailiffs. Keywords: the value of a real property, the price of a real property, enforcement proceedings conducted by bailiffs, description and estimation, methodology and estimation rules

Proses Gentrifikasi Di Perkotaan Yogyakarta

Gentrification is a process of socio-economic transformation in which areas previously inhabited by low-income or low-middle-income residents are gradually inhabited by higher-income residents. This research combines a case study methodology with a qualitative approach to examine the dynamics of social, economic, and physical changes in several areas affected by gentrification in Yogyakarta, such as Prawirotaman, Kotabaru, and Sosrowijayan. Through in-depth interviews, data were gathered with local residents, entrepreneurs, and government officials, as well as analysis of urban planning documents and statistical data. The results show that gentrification in Yogyakarta is triggered by number of causes, including an increase in the tourism sector, investment in property, and government policies that encourage slum revitalization. The impacts of gentrification include increased property prices, displacement of indigenous residents, changes in cultural character, and social conflicts between newcomers and longtime residents.

A Hedonic Approach to Coastal Resilience in Post-Hurricane Maria’s Puerto Rico

Hurricane Maria devastated Puerto Rico in 2017, resulting in reduced beach width and dune flattening in certain coastal areas. Economic development activity observed in coastal regions of Puerto Rico after Hurricane Maria has partly focused on commercial and housing construction projects. The following study seeks to estimate the implicit value attributed by coastal homeowners to natural infrastructure attributes that may serve not only for recreation, but also as protection from future extreme weather events. We estimated a hedonic valuation model to assess the implicit value attributed by coastal homeowners to key natural attributes, emphasizing beach width and property elevation. Property owners sufficiently close to the coastline (1,000 m or less) implicitly assigned a positive value to beach width at their closest coastal location. An unexpected result was that elevation, when interacted with beach and human-made coastal types, was negatively associated with property prices, an indication of the importance of closeness to sea level. Given the impact of extreme weather events in coastal regions, hedonic valuation data on natural coastal attributes such as beach width can provide valuable information not currently considered in coastline development permitting.

Application and Performance Comparison of Compound Neural Network Model based on CNN Feature Extraction in House Price Forecast

Abstract. This study used a total of eight machine learning algorithms to forecast property prices, it not only provides a robust comparison of the predictive power of different algorithms but also significantly advances our understanding of the factors that influence property prices. In this paper, four traditional machine learning algorithms and four neural network models are selected for comparative study and analysis, of which the neural network models include fully connected neural networks (FCNN), convolutional fully connected neural networks (FCNN+CNN), generative adversarial fully connected networks (FCNN+GANs) and generative adversarial convolutional fully connected neural networks (FCNN+GANs+CNN). This study applied to a Kaggle's sample. The results reveal that the models based on FCNN+CNN and FCNN+GANs+CNN perform relatively well in house price prediction, with both obtaining an explanatory power of R as high as 0. 96 and 0. 97, respectively and significantly outperforming traditional machine learning algorithms. It is worth mentioning that the FCNN+CNN model is slightly stronger in terms of error minimization, but both perform better in terms of stability and generalization capabilities. The conclusion is that neural network models generally have better results than traditional algorithms in house price prediction, and the neural network model of CNN composite has significantly better prediction performance.

Social transformation and studentification: a portrait of gentrification in jatinangor education area

The presence of a large university and an increase in the number of students have driven significant changes, including shifts in land use, increases in property prices, and changes in patterns of social interaction between students and local residents. This research aims to examine the social, economic, and cultural impacts of studentification and gentrification in the Jatinangor education area. Using a qualitative approach through in-depth interviews, field observations, and secondary data analysis, this research explores how these transformations affect Jatinangor's community character and cultural identity. The results show that studentification in Jatinangor not only brings new economic opportunities, but also triggers social challenges for the indigenous population, especially related to economic pressures and cultural changes. This study makes an important contribution to the understanding of the dynamics of gentrification in educational areas and provides policy recommendations to support the balance between educational development and socio-cultural sustainability for local communities.

Who bears the burden of real estate transfer taxes? Evidence from the German housing market

This paper examines the effects of real estate transfer taxes (RETT) on property prices using a rich micro dataset of roughly 17 million German properties for the period from 2005 to 2019. Our empirical analysis exploits variation in RETT rate hikes across German states and over time. Our monthly event study estimates indicate a price response that strongly exceeds the change in the tax burden for single transactions. Twelve months after a reform, a one percentage point increase in the tax rate reduces property prices by on average 3%. Price effects are larger for apartments (−4%) than for single-family houses (−2%). Exploring potential mechanisms, we provide evidence that different holding periods are the main driver of the differential price effect between property types.

The Amenity Value of Bicycle Infrastructure: A Hedonic Application to Greater Manchester, UK

Using hedonic and spatial regressions, this paper estimates a significantly larger association between proximity to bicycle networks and property prices than previously reported. As cities face increasing challenges of congestion and pollution, many are implementing policies to integrate bicycle facilities and other active modes of transport. However, policymakers are slow to support these initiatives and remain skeptical due to the investment costs required and appropriation of limited land. Drawing on a large dataset of approximately 253,000 transactions in Greater Manchester, over a 9-year period, we find clear evidenced that a 1 km reduction in distance to the nearest bicycle network is associated with property values being around 2.8% higher, on average, and 7.7% higher in the central borough of Manchester. We also provide an applied example to rank new bicycle routes by comparing their benefit-to-cost ratios and a discussion on the property tax system.

Exploring the Impact of Regional Environment and Institutional Factors on Operation Period of Nursing Facilities in Taiwan.

This study examines factors influencing the operational durations of nursing facilities in Taiwan. It departs from previous closure-focused research by analyzing the duration of facility operation. A Cox regression model was employed to assess the impact of regional, governmental, and individual institutional factors on 199 facilities across diverse areas in Taiwan from 2011 to 2019. Results demonstrate closure hazards associated with rising property prices, particularly for rented facilities, with metropolitan locations exhibiting superior survival rates and lower institutional appraisal grades significantly increasing closure. Additionally, operator background, facility type, and bed occupancy emerged as influential factors. These findings underscore the financial vulnerability of rented facilities to market fluctuations and the challenges associated with adjusting resident fees. Furthermore, the study highlights the unique challenges faced by facilities in low-density areas regarding resident access to resources. Policymakers should closely monitor regional factors and consider proactively implementing early warning systems to address these challenges.

The Impact of Unnatural Death Stigmatization on Neighboring Property Prices: Propensity Score Matching and Difference-In-Differences Analysis.

This study primarily investigated the impacts of unnatural in-house deaths on nearby property prices. Propensity score matching was used to assess the homogeneity of experimental and control groups, and the difference-in-differences (DID) method was used for their comparison. Data for real estate transactions in Taipei City from 2012 to 2022 were retrieved from the Ministry of the Interior's real estate actual transaction price registration query system. The DID interaction variable coefficient of 0.066 was significant at the 10% level. A comparison of the experimental and control groups showed a 6.8% property price difference following an unnatural in-house death. This suggests that an unnatural in-house death has a positive impact on property prices nearby. There were four unnatural deaths at the Xining Public Housing from 2003 to 2012 and four at the Jinxing Building from 1984 to 2012. The high incidence of events and the disclosure of information may have depreciated nearby property prices. The study results show that the stigmatization of unnatural in-house deaths reduces nearby property prices. The depreciation of implicit prices reverted and followed a U-shape pattern after several years.

The effect of subsidized flood insurance on real estate markets

AbstractSubsidized insurance against extreme weather events improves affordability among households in at‐risk areas but it can weaken the risk signal via property prices. Leveraging a granular data set of all property transactions and flood events in England, we study the effects of a reinsurance scheme that lowers insurance premiums for at‐risk properties. We document that the introduction of this scheme increases prices and transaction volumes of flood‐prone properties. This fully offsets the negative direct effects of flooding on property prices, with high‐income areas and high‐value properties benefiting relatively more. Our findings speak to the debate on climate adaptation policies and their consequences for wealth distribution.

Real Estate Inflation and Housing Finance in India : Mapping the Correlation between Raising Property Prices & Increasing Home Loans among Indian Cities

Focusing specifically on India — a country that has recently faced recessions partially due to housing bubbles, like many other nations around the world — this research explores in depth how development of real estate linked financing sector have fuelled mortgage led asset price inflation across different cities (both metros & Tier II/III) over decades. Drawing upon a variety of market statistics, policy and economic indicators, the research identifies strong relationships between housing loan availability — by way of loosening mortgage regulations — and property price growth. The study shows that metropolitan cities have witnessed growth in housing loans at a CAGR of 10-12% over last decade (2010–2020) resulting into similar annual price appreciation for real estate standing between 010-15%. Tier II and III cities alternatively, exhibited slower but more stable growth trends in property prices of 6-8% driven by government push & infrastructure developments. The research emphasizes how this relationship has been altered by a number of variables, including as urbanization, demographic changes, government housing programs, and regulatory reforms like RERA and GST. The results imply that although housing financing has played a significant part in promoting the expansion of the real estate market, the influence varies greatly depending on local market dynamics, infrastructural development, and the state of the local economy.