Residential Rents Research Articles

Machine learning applied to estate pricing for residential rentals in dynamic urban markets—The case of São Paulo city

This study conducts a comprehensive investigation into real estate rental pricing in São Paulo city, employing an innovative approach that combines advanced machine learning techniques with geospatial and natural language processing (NLP) analyses. The research analyzed a robust dataset comprising 47,243 rental listings, gathered through web scraping techniques. Following a rigorous data cleaning and preprocessing procedure, the study focused on 35,486 instances, incorporating a variety of variables that go beyond conventional metrics, including textual descriptions and geographic information, enriching the analysis and market understanding. Several regression models were implemented and compared, including linear approaches, Support Vector Machines, and ensemble methods such as Gradient Boosting, LightGBM, and XGBoost. The Blending model, which integrates multiple modeling techniques, stood out as the most accurate, achieving a Root Mean Squared Logarithmic Error (RMSLE) of 0.2923 on the test set. This result emphasizes the superiority of hybrid modeling strategies in complex pricing tasks. The findings of this study have significant practical implications. They provide landlords and tenants with a powerful data-driven tool for informed decision-making, reflecting the nuances and complexity of São Paulo’s real estate market. The practical implementation of the model in an interactive web application not only demonstrates its utility in the real-world scenario but also serves as a model for future applications in real estate analysis. This work contributes to mitigating the waste of time and energy when it comes to searching for and pricing residential rentals in a large city, through the use of machine learning that shows its power and potential in accurately estimating rental prices in dynamic urban markets, allowing that more assertive and economical decisions can be taken within a social-sustainable-technological perspective.

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

Determinants of housing demands and residential rent costs in an emerging city in Southern Nigeria

Housing is vital to human experience. It is the basis and foci of daily activities. In Asaba, a strategically located, fast-growing metropolis, city officials are confronted by myriads of urban planning issues, one of which is provision of urban housing. This study attempted to identify the determinants of housing demands and costs of residential rents within the city (Asaba). In this study, the ex-post facto and survey designs were deployed. The data for the study comprised of the primary (questionnaire data) and secondary (historical housing data) types. For effective questionnaire administration, the study area was stratified into high, medium and low income areas. The Analysis of Variance (ANOVA) and the Spearman's rank correlation coefficient were deployed for data analyses. The results indicated that the key determinants of residential housing demand and costs were ease of access to public transport (69 %), nearness to security formations (80 %) and higher demand for houses in supply (75.8 %); and the Spearman's rank correlation showed that these factors were significant at P < 0.05. Other factors such as availability of electricity and access to water supply were not significant determinants of choice of housing at P > 0.05. This is due to the fact that the whole region lacks adequate electricity supply and water is self-harnessed through boreholes. ANOVA showed that there was a significant temporal variation in the cost of residential housing rents at p<0.05 (F-69.03; sig 0.000). Consequently, this study recommends that there is need for the decentralisation of the city; which should be realised by an efficient development of the suburbs to high standards that would meet the requirements of prospective renters. Policy development and establishment of basic facilities in the suburbs, would help actualise this decentralization. Also, collaboration between government and private developers could yield more supply in the housing stuck, consequently reducing costs of housing.

Short-term rentals and residential rents: evidence from a regulation in Santa Monica

Purpose This study aims to investigate the relationship between Airbnb and long-term residential rents, using Santa Monica, California, as a case study. In 2015, Santa Monica adopted the home sharing ordinance (HSO), a stringent regulation aimed at restricting short-term rentals (STR). This research examines the implications of this ordinance on the local housing market. Design/methodology/approach The synthetic control method (SCM) is applied to a panel data set comprising Airbnb listings and residential rents from multiple cities in Los Angeles County. This approach is used to estimate the causal effects of Santa Monica’s HSO on two outcomes: Airbnb listings and residential rents. Findings The empirical results show a 60% reduction in Airbnb listings in Santa Monica within two years of implementing the ordinance. Despite this significant decrease, the effect of the regulation on rents was not significant. Suggestive evidence indicates that the ordinance’s ineffectiveness in increasing the number of houses allocated to long-term tenants may have contributed to its negligible impact on rental rates. Originality/value To the best of the author’s knowledge, this research is the first to use the SCM for evaluating the impact of STR regulations. It offers crucial insights to policymakers on regulating platforms like Airbnb. The study reveals a scenario where a marked decrease in Airbnb activity did not lower residential rents, highlighting the need for context-specific evaluations in understanding the housing market’s dynamics. Additionally, these findings are valuable for investors considering the implications of regulatory changes in the STR sector.

Monthly Estimations in U.S. Residential Rental Markets and Affordability

Rental markets around the country have recently spiked, causing significant issues in terms of affordability and availability and even prompting talk of nationwide rent controls. The drivers of this recent phenomenon will almost certainly be parsed out in future research. However, there is a critical and pressing need for timely information on recent rent movements in the nation’s rental markets and their affordability. This brief provides information (projected rents, degree of over- or underpricing relative to historic trends, year-over-year rent change, and affordability of the average rental unit) for 100 U.S. residential rental markets. A sample of the April 2023 Miami metro market is provided in this brief to deliver guidance on these estimations. Monthly reports are currently provided in the Waller, Weeks and Johnson Rental Index. The goal of the index is to facilitate more informed decision making by consumers, real estate professionals, and policymakers.

Hedonic Approach to Vertical Residential Rentals in the Brazilian Amazon: The Case of Belém, Pará

This study set out to identify and analyze the factors that influence the formation of rental prices for residential apartments in the city of Belém. The approach adopted was based on the hedonic price theory, which considers that the rental price of an apartment reflects the implicit prices of its attributes—structural, locational, and neighborhood quality. The sample used consisted of 259 observations, corresponding to the rental advertisements of the representative apartments in each building. The ordinary least squares (OLS), spatial lag model (SLM), and geographically weighted regression (GWR) techniques were used in the statistical analysis in this study. The results of the OLS model showed statistical significance between the attributes analyzed and the rental price of the apartments. In turn, the SLM indicated that the structural attributes have an impact on the rental prices of neighboring apartments, configuring a contagion effect in the real estate market. The GWR model showed that there was no spatial heterogeneity in the effects of the determinants on apartment rental prices throughout the sample.

Private Law as a Tool to Overcome Split Incentives on Energy Efficiency in the Residential Rental Market

Private Law as a Tool to Overcome Split Incentives on Energy Efficiency in the Residential Rental Market

Influence of Location Externalities on Residential Rental Values: Evidence from Nairobi City, Kenya

Location externalities refer to the effects of a particular location on the value, desirability, or quality of neighbouring properties and the well-being of residents in the area. This study investigates the influence of significant location externalities on residential rental values in Nairobi City, Kenya. The methodology employed is a questionnaire survey on a sample of 347 residential tenants within 36 suburbs in Nairobi city. The data are analysed using quantitative methods and presented in tables. According to the study results, residential rental value is significantly influenced by clusters of factors related to the environment, social as well as economic aspects. Specifically, air pollution, crime and insecurity, anthropogenic noise pollution, access to internet connectivity, urban traffic congestion, and inappropriate domestic solid waste disposal are significant location externalities influencing residential rental values in Nairobi City, Kenya. The study recommends regulation and enforcement as well as pricing mechanisms as strategies for managing location externalities in urban areas in Kenya

GERPM: A Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model

Accurate prediction of urban residential rents is of great importance for landlords, tenants, and investors. However, existing rents prediction models face challenges in meeting practical demands due to their limited perspectives and inadequate prediction performance. The existing individual prediction models often lack satisfactory accuracy, while ensemble learning models that combine multiple individual models to improve prediction results often overlook the impact of spatial heterogeneity on residential rents. To address these issues, this paper proposes a novel prediction model called GERPM, which stands for Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model. GERPM comprehensively analyzes the influencing factors of residential rents from multiple perspectives and leverages a geographically weighted stacking ensemble learning approach. The model combines multiple machine learning and deep learning models, optimizes parameters to achieve optimal predictions, and incorporates the geographically weighted regression (GWR) model to consider spatial heterogeneity. By combining the strengths of deep learning and machine learning models and taking into account geographical factors, GERPM aims to improve prediction accuracy and provide robust predictions for urban residential rents. The model is evaluated using housing data from Nanjing, a major city in China, and compared with representative individual prediction models, the equal weight combination model, and the ensemble learning model. The experimental results demonstrate that GERPM outperforms other models in terms of prediction performance. Furthermore, the model’s effectiveness and robustness are validated by applying it to other major cities in China, such as Shanghai and Hangzhou. Overall, GERPM shows promising potential in accurately predicting urban residential rents and contributing to the advancement of the rental market.

Walking Accessibility to the Bus Stop: Does It Affect Residential Rents? The Case of Jinan, China

It is a crucial question to understand the relationship between public transit and residential rents for the proposal of a sustainable transportation system and efficient allocation of lands during the policy marking process. Little has been discovered in the current literature regarding the impact of the bus system on residential rents. This study investigated walking accessibility to the bus stop based on the average daily on-ridership data of bus stops and street networks in Jinan, China, and analysed the impact on the spatial differentiation of residential rents using the spatial autocorrelation analysis and the geographically weighted regression (GWR) method. Our results suggested that residential rent levels in Jinan had evident spatial dependence and spatial differentiation characteristics, which was signified by a significant high rent, and a high accessibility distribution pattern surrounding both city and sub-city centres. GWR results further showed that walking accessibility to the bus stop could significantly improve residential rents. On the spatial scale, a 1% increase in walking accessibility could result in a premium of up to 0.427% and a 2.984% decline in rental prices. Lastly, we found that walking accessibility to the bus stop significantly affected housing rents incrementally with increasing distance between residences and the city centre. Moreover, walking accessibility to the bus stop showed a marginal ‘first increase and then decrease’ effect on residential rents as the distance to the bus stop increased. The premium effect was the most significant among residences within 500–900 m of a bus stop.

INFLUENCE OF TAX COMPLIANCE COST ON HOSTEL OWNERS’ COMPLIANCE TO RENTAL INCOME TAX IN LAIKIPIA WEST SUB COUNTY, KENYA

&lt;p&gt;The Government of Kenya through the Finance Act 2015 introduced a simplified rental income tax. This is a monthly tax payable by a resident person for rental income earned from use or occupation of residential property. Upon the gazettement of the Income Tax (Residential Rental Income Tax) Regulations, 2016, the Kenya Revenue Authority embarked on an aggressive campaign to enlist landlords into the Monthly Rental Income Tax regime. However, the Economic Survey 2021 showed that the country’s revenue collecting agency KRA was not able to meet revenue targets as set by the National Treasury. Comparison of National Government Budget Estimates with Actual Out – turns for the fiscal years 2017/2018 and 2018/2019 showed that KRA missed the Budgeted Ordinary Revenue targets by Kshs.128.7 Billion and Kshs.90.1 Billion respectively. Property taxes including rental income tax are part of the ordinary revenue collected by the tax authority. The objective of the study was to investigate the influence of tax compliance cost on hostel owners’ compliance to the monthly rental income tax in Laikipia West Sub County. Quantitative data was collected using questionnaires. A census of the entire population of hostels was used to collect the required data. The collected data was analysed using the Statistical Package for Social Sciences (SPSS). The study established that an increase in tax compliance cost discourages compliance behavior since taxpayers tend to evade the costs either by under-declaring their monthly rental incomes or not declaring any income at all. The study recommends that KRA should scale up the tax payer education and awareness programs to ensure that more landlords are trained on how to file their own returns without having to use tax agents who make the process costly leading to non-compliance.&lt;/p&gt;&lt;p&gt;JEL: H25, H26&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0982/a.php" alt="Hit counter" /&gt;&lt;/p&gt;

Comparing the Impacts of COVID-19 on Residential Rental Market Across Rental Sectors: Evidence from City of Austin

Comparing the Impacts of COVID-19 on Residential Rental Market Across Rental Sectors: Evidence from City of Austin

Economic Relationships Between Selling and Rental Prices in the Italian Housing Market

In this paper the relationship between price and rent dynamics in the Italian housing market is studied. The aim is reached through the implementation of a multivariate autoregressive model (VAR), that makes it possible to explain the interdependencies of multiple time series. The analysis considers a series of macroeconomic variables in the model that in the deductive interpretation of the phenomenon and on the basis of other experiences in current literature, were evaluated as potential keys to understanding the relationship between prices and rents. The variables selected, along with residential realestate prices and real residential rents, were: the real short term interest rate, the time series of the annual differences between the actual and the expected Gross Domestic Product, real investments in housing. The data series cover the period from 1980 to 2008. The results obtained show some peculiarities of the Italian real estate market.

Do new housing units in your backyard raise your rents?

Abstract There is a growing debate about whether new housing units increase rents for immediately surrounding apartments. Some argue new market-rate development produces a supply effect, which should alleviate the demand pressure on existing housing units and decrease their rents. Others contend that new development will attract high-income households and new amenities, generating an amenity effect and driving up rents. I contribute to this debate by estimating the impact of new high-rises on nearby residential rents, residential property sales prices and restaurant openings in New York City. To address the selection bias that developers are more likely to build new high-rises in fast-appreciating areas, I restrict the sample to residential properties near approved new high-rises and exploit the plausibly exogenous timing of completion conditional upon the timing of approval. I provide event study evidence that within 500 ft, for every 10% increase in the housing stock, rents decrease by 1%; and for every 10% increase in the condo stock, condo sales prices decrease by 0.9%. In addition, I show that new high-rises attract new restaurants, which is consistent with the hypothesis about amenity effects. However, I find that the supply effect dominates the amenity effect, causing net reductions in the rents and sales prices of nearby residential properties.

Social Perception of the Influence of Public Policies on the Residential Rental and Holiday Tourism Rental Markets

This work assesses the influence of generic public housing policies in Spain and specific public policies relating to vacation rental housing on residential rental costs in recent years from the perspective of people linked to residential housing rental and vacation rental housing. We used partial least squares structural equation modelling on the data obtained in a survey of 413 individuals with direct links to the fields of tourism and real estate. The results obtained by the model confirm the influence of the growth in holiday tourist housing and the implementation of public housing policies on rises in residential rent. These conclusions can inform public policy measures in the general area of housing and, specifically, in the area of holiday homes, toward reducing the impacts of certain factors on access to housing in the province of Málaga. The main contribution of this work is the direct collection of the opinions of people related to residential and tourist housing on the Costa del Sol.

The Effect of Public-Housing REITs on Nearby Residential Rent: An Application of Difference-in-Difference and Event Study

본 연구는 공공임대리츠가 인근 아파트 전세가격에 미치는 영향을 분석하는데 그 목적이 있다. 공공임대리츠는 주로 10년 임대 공공임대주택을 공급할 뿐만 아니라 공공임대주택이라는 이미지가 약하기 때문에 우리는 공공임대리츠가 인근지역 주택시장에 부정적 외부효과를 미치지 않을 것으로 기대하였다. 우리는 이벤트 연구 방법을 이중차분법에 적용하여 공공임대리츠의 영향을 분석하였다. 분석결과 우리는 다음과 같은 사실을 확인할 수 있었다. 첫째, 공공임대리츠의 입주는 인근지역 아파트의 전세가격 상승률을 낮추는 방향으로 영향을 미쳤다. 둘째, 이런 영향은 부정적 외부효과에 기인한 것이 아니라 공급증가 효과에 기인하는 것으로 나타났다. 셋째, 공공임대리츠의 입주로 인한 긍정적 외부효과도 어느 정도 있는 것으로 보였다. 본 논문에서는 이중차분법의 평행성 조건(parallel condition)이 충족되는지 여부를 확인하기 위해 공공임대리츠의 입주 이전 시기를 대상으로 플라시보 검정(placebo test)을 해 보았다. 검정 결과 플라시보 효과(placebo effect)는 없는 것으로 나타났다. 그리고 추정계수가 지역별 차이를 보일 수 있어서 위계선형모형으로 모형을 추정해 보았는데, 추정계수가 지역별로 차이난다는 증거를 발견하지 못하였다.

Ensuring the financial and economic security of Russia based on the adjustment of its internal economic imbalances

In recent decades, the Russian economy has been experiencing serious challenges and threats that jeopardise its economic security. Over the years, there has been a noticeable decline in economic growth in the Russian Federation. This study investigates the internal problems of the Russian economy associated with the economic imbalance of its structure. Notably, the key issues of ensuring financial and economic security and the implementation of the economic strategy of the Russian economy concern increasing the efficiency of the use of its economic and social resources. So far it has not been possible to overcome the factor of the raw material orientation of the country's economy; it seems that in recent years this process has slowed down. This is due to some persisting imbalances in the structure of the country's economy. First of all, this can be attributed to the lack of balance in the incomes of the population. Further, as suggested by the study, the state budget is not sufficiently balanced for effective socio-economic development. The next important component of the structural balance of the economy is the condition for the proportionality of prices for vital goods and production costs for these goods. First of all, this implies a chain of interacting prices of socially significant goods and services, such as bread; gasoline; electricity; housing costs; salary; cost of housing and public utilities; residential rent; cost of housing construction (1-2 room apartments of economy class); transport expenses and some others. In conclusion, some recommendations are given for establishing proportions and ratios that contribute to the growth of labour productivity and reduce internal social tensions in society, ensuring economically safe and sustainable development of the Russian economy and society at increasing rates.

Impact of Evictions and Tourist Apartments on the Residential Rental Market in Spain

In recent decades, the analysis of residential rental prices in Spain has gained increasing attention. From a socio-economic viewpoint, the increase in long-term rentals compared to new home purchases by the new generations has led researchers to examine phenomena such as the growth of the tourism sector or foreclosures. This paper uses a panel data model to analyze the influence of the rate of foreclosure evictions and number of tourist apartments on residential rental prices in 50 provinces of Spain for the period 2015–2018. The results show that an increase in the number of tourist apartment vacancies increases residential rental prices, while an increase in the rate of foreclosure evictions causes residential rental prices to fall.

Determining Land Values from Residential Rents

The value of land is determined by the locations’ attractiveness and the degree of direct land use regulation. When regulations are binding, e.g., when a restriction on the maximum floor area ratio exists, the land price can be directly expressed as a function of the maximum floor area ratio and local amenities. We show theoretically and empirically how this approach can be used to determine land values from rental prices of residential structures built upon that land. From our empirical results, we derive two main sources for a monocentric structure of land prices. First, the location attractiveness of centrally located dwellings makes land prices more expensive. Second, as the maximum floor area ratio is high in central areas, the regulation works as a multiplier for land prices and inflates prices accordingly. Our model gives insights into the determinants of urban land prices and provides a useful approach for land appraisal in regions where land transactions are scarce.

Interpretable Machine Learning for Real Estate Market Analysis

While Machine Learning (ML) excels at predictive tasks, its inferential capacity is limited due to its complex non-parametric structure. This paper aims to elucidate the analytical behavior of ML through Interpretable Machine Learning (IML) in a real estate context. Using a hedonic ML approach to predict unit-level residential rents for Frankfurt, Germany, we apply a set of model-agnostic interpretation methods to decompose the rental value drivers and plot their trajectories over time. Living area and building age are the strongest predictors of rent, followed by proximity to CBD and neighborhood amenities. Our approach is able to detect the critical distances to these centers beyond which rents tend to decline more rapidly. Conversely, close proximity to hospitality facilities as well as public transport is associated with rental discounts. Overall, our results suggest that IML methods provide insights into algorithmic decision-making by illustrating the relative importance of hedonic variables and their relationship with rental prices in a dynamic perspective.