Rental Property Research Articles

Impact of COVID-19 on Airbnb occupancy rate in Bandung, Indonesia

The emergence of sharing economy, especially accommodation platform has enable people to rent and capitalize on their private properties by renting to others. However, with the covid-19 pandemic and proximity to first-tier city it is crucial to examine how these sharing accommodation properties survives.The paper investigates how urban neighbourhoods, Airbnb characteristics and urban mobility restrictions determine rental occupancy rates in Bandung, a second-tier city, during the COVID-19 pandemic. This paper examines; (i) the dynamics of determinant factors of occupancy rates before and after the pandemic and (ii) the impact of mobility and spatial proximity to first-tier city on sharing accommodations’ occupancy rates. This study uses econometric and spatial analysis with more than 2200 Airbnb property listings registered between 2016 and 2020 in Bandung city.Our study found a shift of preferences in a more urban direction and electronic word-of-mouth (eWOM) variables continuing with a slight decline in the pandemic. Comparison between before and during the pandemic suggests the persistent of high occupancy rate concentration only in in private listings, and located near to tourist attractions and a variety of amenities. Our finding calls for urban policies that limits the geographical distribution of short-term rental (STR) properties to avoid wider gentrification, safety risks, and pressure to the local housing supply.

Data-driven approach to optimizing property management strategies: spatial modeling analytics of short-term rentals

Purpose This study aims to explore the impact of locational and seasonal factors on the financial performance of short-term rental properties in Margaret River, Western Australia. It seeks to address the gap in understanding how these factors influence key financial metrics such as average daily rate (ADR) and occupancy rates, providing insights for property managers, investors and policymakers. Design/methodology/approach The research uses a mixed-method approach, integrating advanced predictive modeling techniques, such as Random Forests and Gradient Boosting, with spatial clustering algorithms like density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify the clustering structure (OPTICS). The study analyzes a comprehensive data set of short-term rental properties between 2012 and 2019. It focuses on locational attributes, seasonal variations and financial outcomes. Findings The findings reveal that properties located near tourist attractions and amenities consistently achieve higher ADRs and occupancy rates, confirming the critical role of location in driving rental demand. Seasonal analysis indicates significant fluctuations in both ADR and occupancy rates, with peaks during high tourist seasons and troughs in off-peak periods. The study underscores the importance of dynamic pricing strategies to optimize revenue and sustain occupancy across different seasons. In addition, it highlights the influence of property features, such as the number of bedrooms and bathrooms, on ADR, while noting that larger properties do not necessarily achieve higher occupancy rates. Research limitations/implications Future research could expand the scope to include different locations and explore the long-term impacts of locational and seasonal factors on property performance. Originality/value This research contributes to the literature by integrating spatial analysis with advanced predictive modeling techniques to provide a nuanced understanding of how locational and seasonal factors impact financial performance in the short-term rental market. It offers a novel application of data analytics within the context of tourism and hospitality management, bridging theoretical frameworks with practical insights.

Renting trouble? An analysis of crime and calls to police at addresses with different rental types

BackgroundThe number of studies about crime at short-term rentals (STRs), and about crime concentration has surged in recent years, but few have bridged these literatures. This study examines crime and calls for service at STRs in relation to other rentals, hotels, and all other addresses in a university city.MethodsPolice circumstances, rental properties, hotels and motels, liquor licenses, and all other addresses are assessed from January 2021 through June 2023 to understand public safety at STRs compared to other address-types.ResultsSTRs make up less than 10% of rental addresses. Police circumstances are significantly and negatively associated with both distance to campus and to liquor licenses. The maximum unrelated occupancy is positively and significantly associated with issues. STRs have greater issues for their lower maximum occupancy, but other rentals and hotels present substantially more problems on a per-address basis.ConclusionThese findings highlight the scope of STR circumstances, clarify potential differences in crime versus calls for service, and suggest that on a per-address basis, both are substantially worse at hotels and longer rentals than STRs.

Forecasting residential real estate prices and energy usage in Flanders

Purpose Currently, residential forecasting literature is limited with few to no forecasting papers that consider residential prices and energy consumption for the selling and rental market within a single study. The purpose of this paper is to study the prices and energy usage for both markets by combining an explanatory model with the predictions of a predictive model, which will lead to a better understanding of the forecasting results. Design/methodology/approach This study presents an econometric comparison between 508,612 selling properties and 174,007 rental properties transacted in Flanders between January 2019 and December 2023. The authors study their prices and energy usage by combining the causal results of a hedonic price model (HPM) with the predictions of an autoregressive integrated moving average model (ARIMA). Findings The authors find that both markets are extremely sensitive to changes in related factors, whereby selling prices are primarily influenced by macroeconomic factors, but rental prices and especially energy usage are also sensitive to market standards. Policies that target energy efficiency can therefore steer its future trend. In addition, in line with previous literature, the HPM shows an increasingly strong relationship between residential prices and energy efficiency over the years, indicating that these steering energy policies also indirectly monitor residential prices. Originality/value This study underscores the importance of integrating the results of an explanatory model with a predictive model. It demonstrates that combining them provides a more comprehensive understanding which is helpful for policymakers, real estate professionals and investors during their decision-making process.

House Rental Finding System Using Laravel, Vue.Js, And PHP Machine Learning Library

The use of technology in all parts of our lives has greatly increased in recent years, nowadays, people tend to search for everything online before they make decisions. However, looking for rental houses online is not common in Bangladesh, it is not because people do not want to, but because there are not many online platforms focusing on residential rentals. For the mentioned reason we developed a house rental finding web application using Laravel, Vue.js, and PHP-ML library. Laravel handles the backend whereas Vue.js manages the frontend, we utilized PHP Machine Learning Library’s Linear Regression and K-Nearest Neighbours (KNN) algorithm to build a machine learning model for the search system. The goal is to make it easier to find rental properties. Our system gives personalized suggestions based on user inputs, like location, rental price, property size, and the number of rooms and bathrooms. Our application aims to make searching for rental houses a better experience and more efficient

The ‘Airbnbisation’ of European cities: legislation for collaborative accommodations

ABSTRACT This study examines ‘Airbnbisation’ in tourist destinations, where professionally-managed properties have surged, affecting housing availability, prices, and community well-being, along with concerns about tax evasion. Despite platforms like Airbnb intending to support the sharing economy, short-term rental regulations struggle to differentiate between collaborative and professional lodgings, complicating oversight. To address this issue, the study examines regulations in 37 cities and evaluates their effectiveness in differentiating between collaborative and professional accommodations. The research proposes new short-term rental legislation using the results of three quantitative techniques. Data on short-term rental regulations, property locations, features, and host profiles were analyzed. Results show that regulations limiting annual rental days and properties per host encourage collaborative accommodations. However, rules controlling guest numbers, requiring licenses (among others) are not adequate to reduce professionalized lodgings. Collaborative accommodations are often found over 5 km from the cathedral, suggesting they aren't primary contributors to city ‘Airbnbisation’. This research aids policymakers in balancing residents' needs and short-term rental interests.

Rental housing and the continuum of carcerality

Existing research on housing and the carceral state demonstrates a divergence in the carceral state's orientation toward property owners and the unhoused. We focus on the liminal arena of rental housing and draw on three cases—landlords’ use of criminal history to screen rental applicants, citizen participation in policing neighborhoods, and crime initiatives that weaponize building code enforcement—to posit a continuum of housing carcerality. We argue that the carceral regulation of rental housing emerges from sources in civil law and policy, illustrating the enduring relevance of what Beckett and Murakawa call the shadow carceral state. Yet, in the rental context, the carceral state tends to have a more covert, decentralized character which does not consistently align with the economic interests of rental property owners and other housing market elites in comparison to its manifestation at the ends of the continuum.

The social vibe of the tenant/landlord relationship in a ‘tenant market’: the case of Romania

There has recently been a burgeoning interest in private rental housing, yet few studies have addressed the tenant/landlord relationship as lived. This paper contributes related insights from the ‘young’ markets of Eastern Europe where the tenant/landlord relationship is entirely market based but embedded in specific cultural norms and supply/demand balances. Drawing on cultural theories of risk and rich qualitative data from tenants and landlords in Romania, this paper examines narratives of risk and repertoires of risk mitigation practices of the relational risks of renting. Findings portray a supply-rich market where tenants have market power hence financial checks on tenants are socially unacceptable, evictions are rare, and most tenants feel at home in their rented properties. In this tenant market, landlords rely on the social vibe of the first tenant/landlord encounter, showing openness to construct win-win collaborations. These less institutionalised practices seem more inclusive to tenants’ economic unpredictability but high rents push the poor, and ethnic stigmatisation drives the Roma ‘others’ into alternative housing.

A Study on the Differences in Rental Property Selection Based on Gender and the Presence of Children

A Study on the Differences in Rental Property Selection Based on Gender and the Presence of Children

Contrasting luxury effect on urban plant phylogenetic and taxonomic diversity in West African cities

ABSTRACT Urbanization is a driving factor for biodiversity loss and potential climate change by reducing carbon stocks. Understanding how urban development, as mitigated by socio-economic factors alters urban biodiversity is crucial for effective urban planning that maintains or improves environmental resilience. The luxury effect hypothesis predicts that wealthier parts of a city will have higher levels of biodiversity. This effect has been tested widely but we still have limited understanding of how wealth influences urban biodiversity in tropical regions of developing countries where plant species play profound sociocultural roles beyond aesthetics. This study investigates links between household income and the diversity of cultivated plants distribution within neighborhoods of two growing cities in Benin. We conducted a survey of 936 randomly selected households to record their socioeconomic characteristics and survey the cultivated plant species found in household gardens’. This enabled us to estimate household-level diversity metrics including taxonomic diversity and phylogenetic diversity. We found no global support for the luxury effect on phylogenetic diversity but rented properties had lower plant taxonomic diversity along with less phylogenetic diversity than privately owned houses. Taxonomic diversity is higher in the less urbanized areas while phylogenetic diversity is weakened. Household’s cultural connection to plants has a negative effect on both diversity indices. Our results highlight the complex relationships between socioeconomic traits and urban plant diversity distribution in two tropical African cities, which only partly confirmed the luxury effect hypothesis. Disentangling these complex relationships can help city planners and policymakers to take informed decisions to promote sustainable cities.

Analysis Of The Utilization Of Regional Property Leases In Increasing Regional Original Income In Bulukumba Regency

This research aims to analyze the level of effectiveness of the use of rental of regional property (BMD) in increasing the original regional income (PAD) of Bulukumba Regency, to analyze the level of efficiency of the use of rental of regional property (BMD) in increasing the district's original income (PAD). Bulukumba, To Analyze the Economic Level of Utilization of Regional Property Rentals (BMD) in Increasing Original Regional Income (PAD) of Bulukumba Regency. This research uses a descriptive quantitative approach. The type of money data used is secondary data in Bulukumba Regency. The data used in this research are Regional Property Rentals (BMD) and Regional Original Income (PAD). The data analysis technique uses quantitative descriptive using the financial ratio formula: Effectiveness and Efficiency Ratios and economic Ratios. The results of the analysis show that the Bulukumba Regency area is seen from (1) the average Effectiveness Ratio of 37.29 percent, it can be said that the utilization of rental property belonging to the Bulukumba Regency area is not effective (2) the Efficiency Ratio, the average of which is 287 percent, then it can be said that renting regionally owned trunks in Bulukumba district is not efficient. 3) The Economic Ratio is 83.29 percent, so it can be said that renting regionally owned trunks in Bulukumba district is quite economical.

Proptech and the private rental sector: New forms of extraction at the intersection of rental properties and platform rentierisation

Private renting increasingly comprises a complex ecosystem of actors who assemble housing within the market, and collect rental income and data from tenants, and data on the material assets themselves. Our analysis – at the intersection of rentier and platform capitalism – focuses on landed (material real estate) and technological (digital infrastructure and data) property in Australia’s private rental system. Drawing out relationships between the various actors – landlords, rental property managers and real estate agencies, software developers and providers, property developers and investors – and both their properties and their uses of Proptech (property technology), we show how housing and technology are being leveraged for profit in new ways. In Australia, landed property retains its precedence for established (individual and institutional) landlords, whose interest in Proptech relates to enhancing or value-adding to rental housing assets. For Proptech and institutional real estate players seeking to consolidate both landed and technology property, capturing the tech landscape is increasingly important; indeed, securing control and/or consolidation of technology property is a key motivation for building and/or using Proptech among the largest property developers. Our findings show how rent extraction operates across and between different types and scales of property and market actors, and in new ways that differentiate the figure of the rentier while upholding the dynamics of the rentier model.

Risk management in short–term rental properties: methods and strategies for minimizing risks

This article aims to identify and analyze effective risk management strategies within the short–term rental market, focusing on enhancing operational efficiency and profitability while ensuring compliance and security. The study utilizes a methodological framework that integrates data–driven risk assessments, optimization of rental strategies through predictive analytics, the implementation of advanced security systems, and rigorous regulatory compliance. Key findings demonstrate that these strategies significantly mitigate financial, operational, and legal risks, contribute to sustainable business practices, and enhance the market reputation of property managers. The conclusion underscores the necessity of incorporating technological advancements and stringent compliance measures as foundational elements of modern property management. The novelty of this work lies in its comprehensive approach to integrating diverse risk management strategies, providing a holistic view that addresses the multifaceted challenges faced by today’s short–term rental businesses.

Implication of Different Data Split Ratios on the Performance of Models in Price Prediction of Used Vehicles Using Regression Analysis

Introduction; Artificial intelligence (AI) and Machine Learning have become buzzwords lately due to technological changes and data quality testing, especially in shape and finish analysis. Lots of research has been conducted for linear regression algorithms to predict the price in different sectors for share stock, rental properties, prices of used cars etc. This study provides suitable data split ratio for optimum cost estimation based on linear regression model. In present days there is an increasing demand for having own car for every middle class family therefore this have given opportunity to motor vehicle business to offer wide range of used vehicle for re-sale especially companies like Maruti Suzuki, Tata motors & Mahendra motors in Indian motor vehicle industries. Therefore, it is important to know the current value of your car before spending your hard-earned money on any item. Objective; The objective of this paper is finding appropriate value of cars in Metropolitans or even in state capitals. Features like model, mileage, AC, seating capacities, fuel type automatic will be taken into account when doing this. This estimate is designed to help customers find the right options to suit their needs. Methods; We have used a linear regression model to estimate the value of the respective car. Results; For doing this price prediction in this paper using liner regression we have tried to find the optimum accuracy of model by varying data split ratio for training and test data set and concluded with the result that 80/20 ratio is the best ratio with optimum model accuracy for business domain analysis with labelled data set. Conclusion; The findings underscore the importance of careful consideration when selecting a data split ratio for price prediction models in the used vehicle market. The insights gleaned from this study can inform future research and contribute to the development of more accurate and reliable regression models in similar domains.

Shift in house price estimates during COVID-19 reveals effect of crisis on collective speculation

We exploit a city-level panel comprised of individual house price estimates to estimate the impact of COVID-19 on both small and big real-estate markets in California USA. Descriptive analysis of spot house price estimates, including contemporaneous price uncertainty and 30-day price change for individual properties listed on the online real-estate platform Zillow.com, together facilitate quantifying both the excess valuation and valuation confidence attributable to this global socio-economic shock. Our quasi-experimental pre-/post-COVID-19 design spans several years around 2020 and leverages contemporaneous price estimates of rental properties – i.e., off-market real estate entering the habitation market, just not for purchase and hence free of speculation – as an appropriate counterfactual to properties listed for sale, which are subject to on-market speculation. Combining unit-level matching and multivariate difference-in-difference regression approaches, we obtain consistent estimates regarding the sign and magnitude of excess price growth observed after the pandemic onset. Specifically, our results indicate that properties listed for sale appreciated an additional 1% per month above what would be expected in the absence of the pandemic. This corresponds to an excess annual price growth of roughly 12.7 percentage points, which accounts for more than half of the actual annual price growth in 2021 observed across the studied regions. Simultaneously, uncertainty in price estimates decreased, signaling the irrational confidence characteristic of prior asset bubbles. We explore how these two trends are related to market size, local market supply and borrowing costs, which altogether lend support for the counterintuitive roles of uncertainty and interruptions in decision-making.

An Efficient Privacy and Anonymity Setup on Hyperledger Fabric for Blockchain-Enabled Internet of Things (IoT) Devices

The rise in IoT (Internet of Things) devices poses a significant security challenge. Maintaining privacy and ensuring anonymity within the system is a sought-after feature with inevitable trade-offs, such as scalability and increased complexity, making it incredibly challenging to handle. To tackle this, we introduce our proposed work for managing IoT devices using Hyperledger Fabric. We integrated our system on the blockchain with a closed-circuit television (CCTV) security camera fixed at a rental property. The CCTV security camera redirects its feed whenever a new renter walks in. We have introduced the web token for authentication from the renter to the owner. Our contributions include an efficient framework architecture using key invalidation scenarios and token authentication, a novel chain code algorithm, and stealth addresses with modified ring signatures. We performed different analyses to show the system’s throughput and latency through stress testing. We have shown the significant advantages of the proposed architectures by comparing similar existing schemes. Our proposed scheme enhances the security of blockchain-enabled IoT devices and mitigates the single point of failure issue in the existing scheme, providing a robust and reliable solution. Our future work includes scaling it up to cater to the needs of the healthcare system.

How Property Rents and Expenses Depreciate: A Case of Tokyo Office Properties

This is the first comprehensive study on the age profile of new rents, average rents, operating expenses, net operating income, capital expenditure, and net cash flow for office properties in Tokyo. There are four main findings. First, the rate of rental depreciation in Japan is low and explains less than half of the rate of depreciation of property prices, although it is higher in earlier years. Second, average rents exhibit nominal rigidity. Third, approximately half of the observed depreciation in new rents is due to physical deterioration as opposed to functional obsolescence, which is driven by changes in tenant preferences and advances in building technology. Last, operating expenses are independent of age, whereas capital expenditure increases in the first 20 years. Our study contributes to the literature by estimating depreciation rates for commercial real estate rents, costs, and cash flows, with new insights into the detailed age profile and sources of economic depreciation.

Price competition among short-term Airbnb listings in New York City following Local Law 18

ABSTRACT In September 2023, New York City began requiring short-term rental properties to be licenced, wiping off many listings from platforms like Airbnb. The increase in short-term rental rates observed soon after illustrates price rivalry under an exogenous shock to market structure. To quantify that effect, we use data on New York’s Airbnb listings from July 2023 to January 2024 to carry out several fixed-effect regressions. We found that the intensity of price competition is hindered by the presence of multi-listing hosts. Hence, if Airbnb wishes to reclaim its ‘cheaper than hotels’ value proposition, they should tackle excessive market concentration.

Relationship Between Tenant Characteristics and Lease Performance Indicators in Nigeria: A Case Study in Ibadan

This study examined the relationship between tenants’ characteristics and lease performance indicators in Ibadan, Nigeria. The Data for this study were obtained from the Estate Surveying and Valuation firms, and information on tenant characteristics was obtained from the tenant’s acquaintance forms in their portfolio. Out of the one hundred and five (105) Estate Surveying and Valuation firms surveyed, only 85 (80.9%) provided access to the required data for this study. The totality of cases identified (through the records of the Estate Surveying and Valuation firms) suitable for the analysis was one hundred and seventy (170) tenants, which accounted for tenants who were expected to have spent at least two years in their rented accommodation, representing 80.9% complete responses on information on the tenants. The data from the retrieved copies of the questionnaire were analysed using frequency distribution tables, factor analysis, weighted mean score and correlation analysis. Findings from the factor analysis revealed that affordability was the most significant criterion considered by property managers for tenant selection. Findings from the weighted mean score further revealed that affordability was seen as the most significant factor that measures lease performance in the study area, corroborating the findings of the factor analysis. The correlation analysis revealed that there was a strong positive correlation between tenants' characteristics and lease performance. In ensuring the success of rental properties, this study emphasised the crucial role of a meticulous tenant selection process by highlighting tenant characteristics such as tenants' financial capabilities, vetted personal information, solid references and guarantors, and demographic characteristics amongst others that are essential in the tenant selection process. The study recommended that property managers should keenly take into consideration the abovementioned tenant characteristics to ensure lease performance and, in turn, residential property investment performance to ensure the investors meet their investment objectives.

Research on the influencing factors of housing rental prices-take Shanghai housing rents as an example

In todays era, renting offers lower costs and greater flexibility compared to buying, making it a preferred choice for many to meet their accommodation needs. Therefore, identifying factors that affect rental prices is of significant importance both for individuals and governments. Through the establishment of multiple linear regression and stepwise regression models, this paper analyzed 400 samples selected from a dataset of 20,000 rental listings in Shanghai, revealing the relationships between rental prices and key factors in the city. During the process of model establishment and data analysis, variables that significantly impact rent were identified through significance testing. The model indicated a strong relationship between the size of the property and its renovation status with rental prices, while other variables such as proximity to subway stations and the floor level of the property had a weaker impact on rent. These findings can assist individuals in making informed decisions when choosing rental properties. Overall, this research contributes valuable insights into the factors influencing rental prices in Shanghai, enabling better decision-making for both renters and policymakers.