Rent Index Research Articles

Flat rent price prediction in Berlin with web scraping

Internet data pose a challenge to the traditional system of official statistics, which relies on more conventional sources such as surveys and registers, not readily adaptable to rapid changes. Expanding this system to include internet data is currently at an experimental stage, exploring these sources’ potentials and benefits. This paper describes a project conducted within the ESSnet Trusted Smart Statistics – Web Intelligence Network framework. It investigates the use of online apartment listings to analyze the rental market. We used web scraping to extract information from two online real estate portals for flats in the city of Berlin. Using this data, we developed a model to predict rental prices per square meter based on the accommodation’s features and location within the city. We detected offers which appear in both portals by means of statistical matching and removed duplicate offers. Missing values were treated by multiple imputation. The prediction model is a semi-parametric approach where the postal districts are used to describe the location effect. Comparisons with microcensus results and the local rent index reveal significant differences between the market of online flat offers and the stock of existing flat contracts. Interested readers will find the commented programming code in the internet supplement.

Machine Learning Insights: Exploring Key Factors Influencing Sale-to-List Ratio—Insights from SVM Classification and Recursive Feature Selection in the US Real Estate Market

The US real estate market is a complex ecosystem influenced by multiple factors, making it critical for stakeholders to understand its dynamics. This study uses Zillow Econ (monthly) data from January 2018 to October 2023 across 100 major regions gathered through Metropolitan Statistical Area (MSA) and advanced machine learning techniques, including radial kernel Support Vector Machines (SVMs), used to predict the sale-to-list ratio, a key metric that indicates the market health and competitiveness of the US real estate. Recursive Feature Elimination (RFE) is used to identify influential variables that provide insight into market dynamics. Results show that SVM achieves approximately 85% accuracy, with temporal indicators such as Days to Pending and Days to Close, pricing dynamics such as Listing Price Cut and Share of Listings with Price Cut, and rental market conditions captured by the Zillow Observed Rent Index (ZORI) emerging as critical factors influencing the sale-to-list ratio. The comparison between SVM alphas and RFE highlights the importance of time, price, and rental market indicators in understanding market trends. This study underscores the interplay between these variables and provides actionable insights for stakeholders. By contextualizing the findings within the existing literature, this study emphasizes the importance of considering multiple factors in housing market analysis. Recommendations include using pricing dynamics and rental market conditions to inform pricing strategies and negotiation tactics. This study adds to the body of knowledge in real estate research and provides a foundation for informed decision-making in the ever-evolving real estate landscape.

The repeat rent model in the presence of variable deposits

ABSTRACT Accurate measurement of rent indices is essential in various contexts, including inflation assessment and housing market evaluation. We propose a novel repeat rent model specifically designed to accommodate variable deposits, a characteristic common in rental contracts in countries where substantial deposits are commonplace in rental contracts. To address deposit variability, we introduce time-varying deposit-rent conversion rates as additional parameters. Utilizing transaction-level data to apply this model to the rental market in Seoul, South Korea, our rent index reveals a noteworthy trend: rents began increasing in late 2019, predating the COVID-19 pandemic. This finding contrasts with other appraisal-based or survey-based rent indices, which posit that the rent surge began after the pandemic’s onset.

Are Hedonic Models Really Quality-Adjusted? The Role of Apartment Quality in Hedonic Models of Housing Rental Market

Abstract In micro-level data concerning the housing market, the apartment quality may be signaled via textual statements or the attached descriptions/photos. It may be done using hard information related to the easier-to-measure structural characteristics or soft information related to the apartment condition and design - soft quality. This paper checks whether the choice of the approach to handling the issue of soft quality of apartments influences the properties of hedonic models and the course of hedonic rent indices. The study shows that hedonic models that account for soft quality have better statistical properties than those without soft-quality-related variables. Among them, the models that include the information on quality extracted from descriptions of apartments prove to be the best. Still, considerable differences in the indicated course of hedonic rent indices have not been detected. However, the paper concludes that utilizing information on apartments’ soft quality may be crucial to understanding the market adjustment process to economic shocks. It has been proven that the price reaction of the Poznań (Poland) rental market to the COVID-19 pandemic and the Russian aggression on Ukraine has been diversified in the quality-related market segments.

Closing the Rent Index Gap – A Quantitative Approach to Rental‐Sector Gentrification

AbstractThis paper advocates for a greater emphasis on supply sided concepts such as the rent gap in the empirical operationalizations of gentrification. It provides a novel framework to identify rental‐sector gentrification areas through index construction by drawing on insights from the gentrification, rent gap, price index and hedonic regression literatures. The approach is highly adaptable to a variety of regulatory contexts and other housing market idiosyncrasies through the design of the underlying regression model. Drawing on data from the city of Vienna, local rent changes and their relationship to price‐effective transformations of the rental housing supply are quantified. After computing the respective indices, bivariate mapping is utilized to identify potential gentrification areas.

Micro-geographic property price and rent indices

We develop a programming algorithm that predicts a balanced-panel mix-adjusted house price index for arbitrary spatial units from repeated cross-sections of geocoded micro data. The algorithm combines parametric and non-parametric estimation techniques to provide a tight local fit where the underlying micro data are abundant, and reliable extrapolations where data are sparse. To illustrate the functionality, we generate a panel of German property prices and rents that is unprecedented in its spatial coverage and detail. This novel data set uncovers a battery of stylized facts that motivate further research, e.g. on the positive correlation between density and price-to-rent ratios in levels and trends, both within and between cities. Our method lends itself to the creation of comparable neighborhood-level rent indices (Mietspiegel) across Germany.

신규계약 자료를 활용한 비아파트 전ㆍ월세 실거래가지수 작성 방안

In this study, actual contract based indices for non-apartment rental market was estimated which had not been attempted before. At this time, in the case of the monthly rent index, the pure monthly rent index was estimated using data with a deposit ratio of 300 times or less compared to the monthly rent, and both the monthly rent index and the data rolling method were used to improve the stability of the index. As a result, it was confirmed that although there may be some differences depending on the same-house assumption, if sufficient data are collected and the data are properly cleaned in the future, it is possible to estimate a stable chonsei and monthly rent index for non-apartment rental markets. In addition, in order to confirm the changes in the housing rental market after the lease regulation and to control the possibility of showing distorted market information, the chonsei and monthly rent index was estimated using only new contract data. Although the accumulated data series after the lease regulation is short, there is a limit to determining, but there is a difference in rent price level and fluctuation between new contracts and renewal contracts, and it is possible to estimate a rent index that shows the actual price movement.

Housing Rents and Inflation Rates

AbstractThis paper develops a quality‐adjusted measure of marginal housing rents using a monthly statistic of landlord net rental income. The marginal rent index (MRI) exhibits deflation during recessions and leads the official rent index by 7 months. The modified inflation rate based on MRI suggests that the annual official inflation rate was overestimated by 1.7–4.1% during the Great Recession but underestimated by 0.3–0.7% during the subsequent expansionary period.

Early Real Estate Indicators during the COVID-19 Crisis

Abstract In this article we construct a number of early housing-market indicators from daily-scraped list-price data and investigate how they inform on housing-market trends prior to the release of official transaction data. We minimize the impact of non-market conform list prices by our data selection- and cleaning routine and thereby improve the overlap between list and transaction data. In addition to some well-known real-estate indicators (price and rent indices, price-rent ratios, market volume, time-on-market, and market turnover) we develop a new market sentiment indicator which we construct from the direction and frequency of online price changes for individual listings. We then use this group of indicators to investigate how housing markets in London and Vienna react to the COVID-19 pandemic during 2020. For London these indicators show high volatility during much of 2020 and we find that the sales and rental markets drift apart: The sales market shows an overall positive price trend, while the rental market becomes significantly weaker after the onset of the COVID-19 crisis. For Vienna we find that all indicators signal positive market developments for the first eight months after the start of the COVID-19 pandemic.

Rent index forecasting through neural networks

PurposeChinese housing market has been growing fast during the past decade, and price-related forecasting has turned to be an important issue to various market participants, including the people, investors and policy makers. Here, the authors approach this issue by researching neural networks for rent index forecasting from 10 major cities for March 2012 to May 2020. The authors aim at building simple and accurate neural networks to contribute to pure technical forecasting of the Chinese rental housing market.Design/methodology/approachTo facilitate the analysis, the authors examine different model settings over the algorithm, delay, hidden neuron and data spitting ratio.FindingsThe authors reach a rather simple neural network with six delays and two hidden neurons, which leads to stable performance of 1.4% average relative root mean square error across the ten cities for the training, validation and testing phases.Originality/valueThe results might be used on a standalone basis or combined with fundamental forecasting to form perspectives of rent price trends and conduct policy analysis.

Housing preferences during the pandemic: effect on home price, rent, and inflation measurement.

With shelter comprising a one-third weight in the Consumer Price Index, an accurate measure of rent change is essential for determining factors affecting inflation measurement, economic policy, and consumer and business decisions. The pandemic led to a shift in consumer demand for more residential space, both in the home’s interior and exterior. With remote work severing the need to be located near the place of employment, some households opted for more space in lower-density areas, moving out of high-rise structures in the urban core to suburban and exurban single-family and low-rise homes, altering the price and rent-growth patterns among single-family detached, attached, and multifamily properties. While measures of rent change are available for multifamily residential properties, none exist for the single-family rental market, which makes up one-half of the residential rental market. The CoreLogic Single-Family Rent Index (SFRI) fills the gap in rent measurement. The SFRI is a repeat-transaction rent index for single-family homes and is available monthly for the U.S., by major metros, by rent price tier and by property type. The SFRI reveals that after 12 months of the pandemic annual rent growth for detached properties was more than 5 percentage points higher than for attached properties. Substituting the SFRI for Owners’ Equivalent Rent, we find that Core CPI inflation would be nearly 2 percentage points higher by mid-2021. To the extent the rapid acceleration in single-family detached rent growth has yet to be reflected in the CPI, inflation measurement will be understated with delayed signals for economic policy makers.

Location, location, location!*: a quality-adjusted rent index for the Oslo office market

PurposeCommonly used rent indices are based on average developments or expert opinions. Such indices often suffer from compositional biases or low data coverage. The purpose of this paper is to overcome these challenges using the authors' approach.Design/methodology/approachThe authors construct a quality-adjusted rent index for the office market in Oslo using detailed data from 14,171 rental contracts.FindingsThe authors show that compositional biases can have a large impact on rental price developments. By adding building-fixed effects to a standard hedonic regression model, the authors show that the explanatory power increases considerably. Furthermore, indices excluding location-specific information, or which include less granular location controls than at the building level, portray quite a different picture of rent developments than indices that do take this into account. The authors also exploit information on contract signature date and find that a more timely detection of turning points can be achieved by using the signature date instead of the more typically used start date of the lease.Research limitations/implicationsThe study is confined to Norwegian data, and an avenue for future research would be to explore if similar results are obtained for other countries. A weakness with the paper is that authors' do not observe quality changes over time, such as renovation. Controlling for time-varying and unit-specific attributes in hedonic models for the commercial real estate (CRE) market would be useful to purge indices further for compositional effects and unobserved heterogeneity. While the authors do control for building-fixed effects, there are additional variations within a building (floor, view, sunlight, etc.) that the authors do not capture. Studies that could control for this would certainly be welcome, both in order to estimate the value of such amenities and to see how it affects estimated rent developments. Another promising avenue for future research is to link data on rental contracts in the CRE market with firm-specific information in order to explore how firm profitability and liquidity may affect rental contracts.Practical implicationsThe authors show that the hedonic index yields a sharper fall in rents after the global financial crisis and more muted developments in the period between 2013 and 2015 than the average rent index. The results show that rents have followed their estimated equilibrium closely and have re-adjusted quickly in periods of deviation. From a financial stability perspective, the risk of a sharp fall in rents is reduced because rents often are in line with their fundamentals.Social implicationsThe authors find that a more timely detection of turning points can be achieved by using information on the signature date. This is an important finding. The financial system is heavily exposed toward CRE, and timely detection of turning points is critical for policymakers.Originality/valueThe financial system is heavily exposed toward the commercial real estate market and timely detection of turning points is of major importance to policymakers. Finally, the authors use our quality-adjusted rent index as the dependent variable in an error correction model. The authors find that employment and stock of offices are important explanatory variables. Moreover, the results show that rents have followed their estimated equilibrium path.

Why is the rent so darn high? The role of growing demand to live in housing-supply-inelastic cities

Real rents measured in the United States CPI increased 17.4 log-points from 2000 to 2018. We present a spatial equilibrium framework to decompose the increase into several channels, including demand to live in housing-supply-inelastic cities. We find location demand contributed significantly: using parameterizations from the literature and a new rent index, we find it is responsible for between 17 and 73 percent of the overall rent increase, and an even larger share in cities where CPI is measured. The wide range is primarily due to a lack of consensus over the population elasticity to rents, so we estimate it by comparing the effects of demand shocks across cities of differing housing supply elasticities. We find that demand changes have similar effects across cities, suggesting a high population elasticity. Therefore, our preferred estimate is that location demand accounts for more than half of the increase. We discuss implications for housing supply policy.

A hedonic assessment of the relative importance of structural, locational and neighbourhood factors on advertised rents in Malta

PurposeThe private rental market in Malta has expanded significantly in recent years, but as at 2020, no official rent index is yet published. This paper aims to construct such an index and explores the relative importance of structural, locational and neighbourhood factors to advertised rents.Design/methodology/approachThe authors compile hedonic indices for advertised rents in Malta collected from publicly available sources using webscraping techniques. The database comprises more than 25,000 listings with information on various property attributes. Hedonic regressions are estimated using ordinary least squares and rent indices are computed using three alternative methods: the time dummy method, the rolling time dummy method and the average characteristics method. For the latter, indices are computed using the Laspeyres, Paasche and Fisher methods.FindingsThe results from the hedonic indices indicate that the annual growth rate in advertised rents was slowing down during 2019, albeit still remaining relatively high, while in 2020, advertised rents contracted sharply, amplified by the effects of COVID-19. The findings also reveal that advertised rental prices are significantly influenced by various structural, locational and neighbourhood factors.Originality/valueThis paper introduces the first rent index in Malta that will be used to monitor developments in the rental segment of the housing market and for financial stability purposes given the share of buy-to-let properties. It also provides various elasticities on the impact of property attributes on advertised rents in Malta. Finally, the study contributes to the literature on the effect of foreign-born residents on advertised rents.

Detection of price bubbles in Istanbul housing market using LSTM autoencoders: a district-based approach.

Since the early 2000s, there has been a long-term price increase trend in the Istanbul housing market, and this situation also has led to price bubble speculations. Since the housing sector was caught with a high level of unsold housing stock to the economic slowdown emerging in the second half of 2018, housing price bubble speculations have increased even more, especially for the Istanbul market. In this period, housing loan interest reduction campaigns were implemented by the government through state banks to stimulate the housing demand, and a probable collapse in the housing market was prevented. On the other hand, house prices continued to rise during this period due to the stimulated demand. In this paper, we perform a price bubble research on the selected districts in the Istanbul housing market over the 2007–2019 period using LSTM autoencoder model. The first analysis on monthly data is performed by using housing price index, housing rent index, consumer prices index, stock market index, return on government debt securities, USD/TRY exchange rates, BIST price index, monthly deposit interest rates, mortgage interest rates and consumer confidence index, and the second analysis on quarterly data is carried out by adding building construction cost index and GDP data to the previous dataset. In the first analysis, the bubble formations differ regionally and periodically and disappeared toward the end of 2019 in some districts, while in the second analysis, the housing bubble formations have a more common and continuous appearance. Experimental results show that LSTM autoencoder model can be used to detect housing bubbles effectively.

Micro-Geographic Property Price and Rent Indices

Micro-Geographic Property Price and Rent Indices

Location, Location, Structure Type: Rent Divergence within Neighborhoods

Housing rents are a large share of household budgets and make a large contribution to overall inflation. Rent inflation rates for different types of housing units sometimes diverge, even in the same neighborhoods. We estimate during 2013 to 2016 apartment rents outpaced rents for detached housing in the United States by 0.76 percentage points annually after controlling for location effects. These rent dynamics imply a segmented housing market. They also suggest rent indexes need to be based on data structurally representative of their measurement objective. In particular, indexes based on professionally managed apartment complexes mismeasure the rents for housing generally. Even indexes based on careful geographical sampling, such as the Consumer Price Index’s Owners’ Equivalent Rent component, may be biased by using an unrepresentative mix of apartments and houses.

Can the BH&J Buy vs. Rent Index Anticipate Housing Price Movements?

This paper investigates whether and to what extent the Beracha, Hardin, and Johnson Buy vs. Rent Index (BH&J Index) is able to anticipate future housing price movements. The BH&J Index is based on a model introduced by Beracha and Johnson (2012) in which a low index value is associated with a buy recommendation and a high index value is associated with a rent recommendation. The results of our analysis reveal a negative relation between the BH&J Index value and future housing price changes at the metropolitan level, which is consistent with the buy or rent recommendation of the index. The negative relation between the BH&J Index value and future housing price changes is statistically significant, economically meaningful, and is particularly relevant for medium-term holding periods.

Why is Poverty Higher in Rural America According to the Supplemental Poverty Measure? An Investigation of the Geographic Adjustment

Poverty is a key indicator of economic hardship. By providing a geographic adjustment for cost of living, the recently developed Supplemental Poverty Measure has upended long-held views that poverty is higher in rural compared to urban America. In this study, we unpack the geographic adjustment underlying the Supplemental Poverty Measure and find that most of the difference is explained by the median rent index component rather than the housing tenure component. Further, we find that six states (Alabama, Georgia, Kentucky, Mississippi, North Carolina, and Ohio) account for over a third of the nonmetro poverty drop due to the median rent adjustment. We demonstrate that the demographic composition of the rural poor remains relatively unaffected despite this large drop, but find that the prevalence of poverty within demographic groups varies considerably. As a result, while the SPM is largely considered a more complete measure of poverty, the use of the median rent adjustment has important implications for the demographic understanding of rural poverty.

Subdivided Rent Indices for the Monthly Rent with Variable Deposit Market

향후 베이비부머 세대의 은퇴와 맞물려 인구의 고령화, 주택가격의 안정, 소득 정체 상황이 유지될 경우 다수의 기관에서는 향후 임대 시장이 월세 중심으로 재편될 것이라고 전망하고 있다. 보증부월세는 전세나 완전월세와 같이 하나의 단일 가격이 아니라 보증금과 월세의 조합에 의해 다양한 스펙트럼의 형태로 존재한다. 이러한 이유로 기존의 월세지수는전월세전환율을 이용하여 전환된 완전월세가격으로 지수를 산정하여 왔다. 그러나 완전월세에 기초한 지수는 다양한 보증금의 비중이 존재하는 보증부월세시장의 변동 추이와 괴리되는 현상이 두드러지는 한계가 있다. 이와 같이 국내 월세시장에 대한 이해와 관련 시장지표가 부족한 상황에서 전세의 급등과 월세의 하락으로 촉발된 기존월세지수의 체감도에 대한 논란이 제기되었고, 주택 임대시장의 임대료 변화를 단일지표로 표현할 수 있는 합리적이고 현실적인 임대료 지수의 필요성이 대두되었다. 따라서 본 연구는 기존 월세 관련 통계의 문제점을 살펴보고 실거래가인 전월세확정일자신고 자료를 이용하여 보증금 비중별로 세분화된 임대료지수를 개발한다. 개발된 세분화된임대료지수는 서울시 아파트를 대상으로 지수를 산정한다. 본 연구의 방법론은 향후 지역별 및 유형별로 확대될 전월세 실거래가지수를 산정하는데 있어 초석이 될 것으로 기대한다.