Online Real Estate Research Articles

Study on the mechanism of non-immersive VR technology's impact on consumer decision-making ——Take high-intervention goods for example

This study explores the impact of non-immersive VR in online real estate platforms on commodity consumption decisions. Non-immersive VR displays simulated environments on screens, with users interacting via keyboard and mouse. BEKE, a prominent Chinese real estate platform, introduced VR house-viewing in 2018, marking its the first user of this technology in e-commerce. This study deeply analyzes the effect of non-immersive VR on consumer decision time, using survival model, tendency matching, and Hecmann control function, to reveal its role in decision-making. The study aims to provide useful insights for the innovative development of the online real estate industry.

Improving The Practice of Real Estate E-Auction in Malaysia: An Exploratory Study

Nowadays, internet auctions are employed instead of traditional auction procedures. This is attributed to information and computer technology advancements, e-commerce technologies, a vast catchment market, and other considerations. The online real estate auction has been used globally and in Malaysia. The High Court of Malaya now uses the E- Auction System for public real estate auctions in Malaysia. However, E- Auction practice in Malaysia has been asked to be discontinued since it interferes with the auctioneer's profession, and bidder data privacy has been compromised, potentially resulting in losses for bidders and others. This research aims to improve the current practice of real estate E- Auctions in Malaysia, with a particular emphasis on the High Court of Malaya E-Auction. Thus, the research objective to be met is to propose the improvements to the practice of real estate E-Auction in Malaysia. Meanwhile, the methodology for this research is based on primary data, which includes journal, book, conference, and seminar papers, by provides a set of questionnaires to the respondents from the public, bidders, and auctioneers themselves. The questionnaire was circulated using Google Forms, and two hundred fourteen (214) individuals participated in the survey as panel in the auction sector. The acquired data would be analysed using descriptive, crosstab, and content analysis. As a result, the research discovered a need to strengthen the auction industry, particularly the E-Auction System High Court of Malaya, in order to guarantee that the real estate auction sector remains relevant and contributes to Malaysia's economic development.

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.

Seller and search behavior in the Belgian housing market

This paper analyzes online real estate listings to estimate the listing price elasticity of search activity for the individual seller for both the rental and sales market. This elasticity is an essential parameter in the literature modeling the process of selling a home, as it indicates the extend to which potential buyers will react to the listing price. Furthermore, we argue that our estimates are an approximation for the average price elasticity of demand facing the individual seller, which is an indication for the amount of competition in the market. A novel dataset tracking real estate advertisements on a daily basis allows to control for unobserved heterogeneity between properties by using property fixed effects, exploiting the variation caused by changes in listing prices and search behavior throughout the listing process. The findings indicate that a one percent increase in the listing price reduces search activity for the property by 6.7 percent for the sales market and 6.6 percent for the rental market. Additionally, we find that an increase in the number of listings in a market segment enhances the listing price elasticity.

The 3 Decades Global Financial Policy Failure in Housing Market, Runaway Home Prices, Today’s Growing Homeless Crisis: Eddison Walters Real Estate Housing Technology Structural Change Transformational Theory

Failure to address technological-driven structural changes in the real estate market that began three decades ago has caused runaway home prices that developed countries are experiencing today. The real estate crash that preceded the Global Financial Crisis, and the entire Global Financial Crisis resulted from the panic that was created about the rapid increase in home prices that was falsely labeled a real estate bubble by the media which was quickly adapted by policymakers. Policymakers and the media blamed greedy mortgage brokers for the rapid increase in home prices, accusing mortgage brokers of providing mortgages to too many people for homes that the borrowers could not afford. It turns out that the media and policymakers were completely wrong when mortgage brokers were falsely blamed for creating a real estate bubble because a real estate bubble never existed. The true cause of the rapid increase in home prices was the early stages of a real estate boom from technological-driven structural changes in the real estate market. Failure to address structural changes in the real estate housing market which started three decades ago, has resulted in runaway home prices in the United States and developed countries around the world. As a result of the failure widespread economic hardship from homelessness is a significant risk as working families find themselves priced out of the housing market. Three decades of economic policy failures and the mismanagement of the housing sector have resulted in a looming housing crisis that continues to increase as developed countries face runaway home prices. Policymakers throughout developed countries eagerly accepted the media-driven misinformation of a real estate bubble without any evidence presented supporting the narrative. The failure of policymakers to gain an understanding of factors that have been driving the rapid increase in home prices which began three decades ago, resulted in the real estate crash, Global Financial Crisis, and Great Recession. Much of the economic hardship and the looming housing crisis facing working families today could have been avoided. For many working American families, the prospect of home ownership no longer exists due to skyrocketing home prices. The looming homeless crisis facing the developed world today is the direct result of economic policy failures that have ignored the structural changes in the housing market for three decades. The move from print media real estate listings to online real estate listings and the adaptation of online mortgage loan applications along with automated mortgage underwriting between 1995 and 1999, exponentially increased the quantity of inventory needed in the housing market of developed countries. The shift in the demand curve for housing structurally changed the housing market in developed countries resulting in the complete transformation that saw the demand for housing skyrocket by 2000. New homebuyers who previously only had access to local housing markets before the transformation of the housing market began in 1995, had full access to homes listed anywhere online in other real estate markets by the end of 1999. The increased demand caused the rapid growth in home prices. Failure to implement policies to drastically increase home inventory in developed countries to address the structural transformation of the housing market which drastically increased the demand for homes was a significant economic policy failure. Instead of implementing policies to significantly increase home inventory, new construction was idle for over a decade as policymakers adopted the media-driven misinformation of a real estate bubble and supported policies to address the nonexistent real estate bubble.

The Economic Penalty of Terrorism: Increase in Discrimination against Arabs and Muslims after Paris Attacks

What repercussions does Islamist terrorism have for Arab/Muslim minorities in targeted countries? We highlight consequences in the domain of online real estate rental. Using high-frequency data on all transactions happening on the largest Parisian online rental market over the period of November 2014 to March 2018, we are able to trace the evolution of prices and occupancy rates over time. We demonstrate that discrimination against properties hosted by Arab/Muslims rises swiftly in the aftermath of the terror attacks in November 2015 and wanes at a much slower pace. We quantify the average financial loss per listing that results from increased discrimination to equal $178 per month. Our findings not only highlight the presence of discrimination in peer-to-peer transactions but also show how sharing economy data measuring these transactions can be used as a “seismograph” to track and measure the development of discriminatory attitudes in response to societal events.

Study on Support Measures for Elderly one-person household Using PropTech

In South Korea, the rapid increase in aging and single-person elderly households is presenting challenges. These challenges are categorized into poverty and economic vulnerability, social isolation, health issues, and housing problems. Solutions utilizing proptech have been proposed to address each of these issues effectively. Proptech offers comprehensive solutions for the elderly, addressing economic and housing stability, social isolation, and health concerns. It provides financial management tools, online real estate consulting, VR technology for virtual tours, and training in various skills to enhance seniors' economic stability and social engagement. For housing, it includes affordable housing search tools, cost calculators, and online payment systems, alongside energy-efficient improvements and smart home technologies for physical safety and comfort. To combat social isolation, proptech utilizes smart devices and online platforms for community engagement. For health, it offers telemedicine, wearable health monitors, and AI-powered analytics to improve healthcare access and preventive care for seniors.

Individual-level home values and cancer mortality in a statewide registry.

Prior work assessing disparities in cancer outcomes has relied on regional socioeconomic metrics. These metrics average data across many individuals, resulting in a loss of granularity and confounding with other regional factors. Using patients' addresses at the time of diagnosis from the Ohio Cancer Incidence Surveillance System, we retrieved individual home price estimates from an online real estate marketplace. This individual-level estimate was compared with the Area Deprivation Index (ADI) at the census block group level. Multivariable Cox proportional hazards models were used to determine the relationship between home price estimates and all-cause and cancer-specific mortality. A total of 667 277 patients in Ohio Cancer Incidence Surveillance System were linked to individual home prices across 16 cancers. Increasing home prices, adjusted for age, stage at diagnosis, and ADI, were associated with a decrease in the hazard of all-cause and cancer-specific mortality (hazard ratio [HR] = 0.92, 95% confidence interval [CI] = 0.92 to 0.93, and HR = 0.95, 95% CI = 0.94 to 0.95, respectively). Following a cancer diagnosis, individuals with home prices 2 standard deviations above the mean had an estimated 10-year survival probability (7.8%, 95% CI = 7.2% to 8.3%) higher than those with home prices 2 standard deviations below the mean. The association between home price and mortality was substantially more prominent for patients living in less deprived census block groups (Pinteraction < .001) than for those living in more deprived census block groups. Higher individual home prices were associated with improved all-cause and cancer-specific mortality, even after accounting for regional measures of deprivation.

How digitalisation influences neighbourhood change

Despite much research on the societal and individual consequences of new digital communication technologies, little attention has been paid to the neighbourhood as a locus of impact. This paper investigates how the growing influence of social media and real estate platforms will likely shape the process of neighbourhood change and the geographic distribution of population and financial resources. The investigation is grounded on a model of neighbourhood dynamics based on flows of households and property investments across metropolitan space, which are guided by imperfect information about housing market opportunities. Decision-makers receive information passively (increasingly through local and non-local social media) and when uncertainty about course of action becomes intolerable they turn to active search (increasingly involving online real estate platforms). Based on this conceptual framing, the article synthesises extant research to draw implications on how the expanding use of these new technologies is affecting neighbourhoods by changing the composition of decision-makers and the information that underpins their decisions. It concludes that digitalisation reduces neighbourhoods residential stability, social capital and diversity, while rendering their dwellings less well-maintained and more expensive.

Antecedents of Real Estate Investment Intention among Filipino Millennials and Gen Z: An Extended Theory of Planned Behavior

The Philippines’ real estate (RE) market vis à vis its government’s collective efforts to accelerate its digital transformation faces prevalent illegal RE practices online on top of limited publicly accessible data for decision-making and decentralized and highly regional RE markets. As the middle-income millennials’ and Gen Zs’ purchasing power rises, these increasingly important consumer groups might fall prey to online RE colorums or make bad RE investment decisions. In anticipation of big data, ML, and AI becoming integral to the Philippine RE industry, this study extends the theory of the planned behavior model to account for RE investment and illegal practice and to build a theoretical basis for foundational models. A total of 400 Filipinos aged 15 to 42 from different regions in the country responded to a self-administered online survey questionnaire. The model was assessed using partial least squares structural equation modeling (PLS-SEM) and was proven to be sufficient to explain the proposed model. Results from the partial least squares structural equation modeling (PLS-SEM) revealed that during inflation, risk tolerance (RT), perceived property value (PPV), and aversion from illegal practice (IP) significantly influence millennials’ and Gen Zs’ intention to invest in the RE market, implying that they would consider prioritizing profitability at the expense of sustainability. Thus, a collective commitment to provide transparent and real-time data on RE KPIs and projects is necessary for safer and optimized operations while ensuring the sustainability of current and future projects.

The impact of energy certificates on sales and rental prices: a comparative analysis

PurposeThe current literature on energy certificates shows that Energy Performance Certificate labels have an important effect on real estate prices. However, interestingly, the limited studies that address the rental market find significantly lower price premiums than the sales market. The purpose of this paper is to add to this literature, by doing a comparative analysis of price premiums in the sales and rental market in Flanders (Belgium).Design/methodology/approachThis study uses a hedonic regression model to analyze 177,670 real estate listings between 2016 and 2021. The data is provided by Immoweb – the largest online real estate platform in Belgium. The data set was divided in sold and rented properties: the authors evaluated 126,217 sales listings and 51,453 rent listings.FindingsThe results confirm that energy efficient properties generate a price premium, but that this premium is significantly larger in the sales market than in the rental market. In addition, the findings indicate that both investors and landlords could benefit strongly from renovating dwellings – especially when renovating from an F label to an A label.Originality/valuePrevious research focuses strongly on the sales market, although in many countries the rental market is similar in size and responsible from much energy consumption. Interestingly, the few studies that are addressing the rental market, find singificantly smaller price premiums than in the sales market. The findings add to this literature tradition and offer a comparative analysis of price premiums in the sales and rental market in Flanders. This allows us to not only show the similarities between both markets but also highlight the differences – creating valuable insights for academia, governments and real estate professionals.

Software application development for real estate activities

The real estate industry is in the early stages of software application, in constant search of new tools to make the industry work more efficiently and effectively. The real estate market is differentiated by certain needs, respectively each segment of the real estate market is looking for software solutions to meet the main needs. In order to meet the requirements of the real estate market, the authors come up with a proposal to create a prototype (iMoPal), including software application development, for a product, namely an online real estate platform for property management, and the creation of a scoring system for tenant and landlord. The application offers a proficient time management solution between tenant and landlord.

Interpretable Multi-Modal Stacking-Based Ensemble Learning Method for Real Estate Appraisal

With the development of online real estate trading platforms, multi-modal housing trading data, including structural information, location, and interior image data, are being accumulated. The accurate appraisal of real estate makes sense for government officials, urban policymakers, real estate sellers, and personal purchasers. In this study, we propose an interpretable multi-modal stacking-based ensemble learning (IMSEL) method that deals with various modalities for real estate appraisals. We crawl the structural and image data of real estate in Chengdu city, China from the nation's largest real estate transaction platform with the location information, including public services, within 2 km of the real estate using Baidu map. We then compare the predictive results from IMSEL with those from previous state-of-art methods in the literature in terms of the root mean square error, mean absolute percentage error, mean absolute error, and coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). The comparison results show that IMSEL outperformed the other methods. We verified the improvement of introducing a data transformation strategy and deep visual features through a 10-fold cross-validation. We also discuss the managerial implications of our research findings.

Proptech: A qualitative analysis of online real estate brokerage agencies in Spain

Purpose: The arrival of new companies that are more technological and scalable to the real estate sector has caused a revolution in a very conservative and not very innovative industry. The main objective of this research is to characterize the business model of proptech online brokerage companies in home sales in Spain, based on the traditional agency model. Design/methodology: Nine semi-structured interviews were conducted with the CEOs and managers of six proptech online real estate brokerage agencies in Spain. Their content was analyzed by means of the study and comparison of common patterns. Findings: During the digitalization process of the brokerage, a regression was detected back to more classic procedures typically found in traditional agencies, thus establishing an important difference as compared to other more transparent markets. Accordingly, a convergence occurs between the different real estate brokerage models (online and traditional), resulting in a hybrid model. Research limitations: The limited number of agencies that operate in the online mode in Spain makes the sample size quite small. Practical implications: The results contribute greater transparency to a traditional sector characterized by its opacity, and they give visibility to the changes that are currently taking place, facilitating information to both professionals and users. Social implications: It contributes to the fulfillment of Sustainable Development Goal number 9, concerning the promotion of development-oriented policies that support the creation of decent jobs, entrepreneurship, creativity and innovation. Originality/value: It expands the scientific literature dedicated to online real estate brokerage in Spain, given that as of today, we have no knowledge of any other published academic work on this topic. The research contributes to the vision that new sector agents have of it and those changes that are occurring within it.

Using machine learning algorithms for predicting real estate values in tourism centers

Along with the development of technology in recent years, artificial intelligence (machine learning) techniques that perform operations, such as learning, classification, association, optimization, and prediction, have started to be used on data on real estate according to the criteria affecting the value. Using artificial intelligence (machine learning) techniques, valuation processes are performed objectively and scientifically. In this study, machine learning techniques were employed to balance the real estate market, affected by the tourism sector in Alanya district of Antalya province, Turkey, and examine changes in value objectively and scientifically. First, the criteria affecting the real estate value were determined as structural and spatial, and data on real estate were obtained from the online real estate website. Then, the values of the real estate in the selected application area were predicted using machine learning algorithms (k-nearest neighbors, random forest, and support vector machines). Unlike studies in the literature, algorithm-based valuation using machine learning algorithms was performed instead of mathematical modeling. When analyzed for performance metrics, the best result was achieved with the support vector machines algorithm (0.73). Objective methods should be used to balance the exorbitant differences between real estate values, to regulate market conditions and to carry out a real estate valuation process free from speculative effects in coastal areas where tourism factor is effective. This study indicated the applicability of algorithm-based machine learning techniques in real estate valuation.

Are Online-Only Real Estate Marketplaces Viable? Evidence from China.

Online businesses have been surging worldwide during the past decade, especially during the recent COVID-19 epidemic. However, the market share of online real estate transactions is still limited, mainly due to the information-asymmetry problem. In this study, we manually collect data on online judicial housing auctions in China, which is currently the largest online real estate market globally, and investigate how information disclosure facilitates real estate transactions. The empirical results suggest that disclosing better quality information online can attract more potential buyers. In particular, providing more comprehensive information such as professional appraisal reports or videos of the property can help to convert buyers’ initial interests into completed transactions and higher sales proceeds. The positive effects of information are particularly strong when combined with offline services, in a more mature online market, and for low-value properties. We also provide preliminary analysis of factors affecting online-information-disclosure quality from both the macro and micro perspectives. We also provide preliminary analysis of factors affecting online-information-disclosure quality from both the macro and micro perspectives.

Improving Real Estate Rental Estimations with Visual Data

Multi-modal data are widely available for online real estate listings. Announcements can contain various forms of data, including visual data and unstructured textual descriptions. Nonetheless, many traditional real estate pricing models rely solely on well-structured tabular features. This work investigates whether it is possible to improve the performance of the pricing model using additional unstructured data, namely images of the property and satellite images. We compare four models based on the type of input data they use: (1) tabular data only, (2) tabular data and property images, (3) tabular data and satellite images, and (4) tabular data and a combination of property and satellite images. In a supervised context, the branches of dedicated neural networks for each data type are fused (concatenated) to predict log rental prices. The novel dataset devised for the study (SRED) consists of 11,105 flat rentals advertised over the internet in Switzerland. The results reveal that using all three sources of data generally outperforms machine learning models built on only tabular information. The findings pave the way for further research on integrating other non-structured inputs, for instance, the textual descriptions of properties.

Automation of Real Estate Registration Using Block Chain Technology

Abstract: Online real estate Registration is a project which provides a friendly format to register land properties. Users can search and browse for property in this application. This application also allows users to see land details on online. This application mainly concentrates on maintaining and managing the details of the property. This application deals with lands details. Now-a-days all applications which require huge security are migrating their application to Block chain as Block chain inbuilt for security data. All existing techniques were not having any facility to check whether data store at decentralized (same data will be saved at multiple nodes and if one node hack or down then data will be gather from other working node) server is intact or change. To check data is intact we can use Block chain technology to maintain land registration details as this technology maintain tree to store each transaction (any data storage will be consider as transaction) and while storing new data then Block chain verify all transaction hash code and if data is not alter or hack then same hash code will be generated and verification will be successful and new block will be added and if data is changed then different hash code will be generate and verification will be failed which indicate node is hack.

Improving online Real Estate Management System using data analytics

As technology advances, every industrial sector has continued to innovate and scale. Several property technologies have fast-tracked nominal developments and accelerated growth in the Real Estate market. Real Estate markets in developing countries have endured challenges and issues emanating from various areas such as management, trust, sales, and infotech, many more. This paper aims to implement a model of the online Real Estate Service system via a responsive web application using data analytics and visualization techniques that show insights and improve decision making based on user data. This implementation includes backend clustering and Regression algorithms to produce insights based on available test data of over 62 PropTech firms in Nigeria, thus helping clients, managers, and agents make critical decisions to accelerate organizational growth. The model was developed by analyzing the impact of data growth in the online Real Estate service system, hinging on the study of earlier literature and data from over 7000 PropTech firms. The research adopted a questionnaire and validation method to understand the main domains to focus on, including property search, Agent trustworthiness, and capabilities. The findings presented here include use cases of the application that provides comprehensive solutions with the system feature providing solutions for deeper property search, minimizing the danger of being scammed and making it easier to acquire properties with specific criteria and to easily contact verified agent based on individual needs of the client and agents track record. Test findings reveal that more data introduced to the system will make it more efficient since the threshold of each level adapts based on data inputted in a dynamic cluster.

Online communities and offline sales: considerations on visiting behavior dimensions and online community types

PurposeAs an increasingly popular tool for product exploration, online communities have an important impact on consumers’ purchasing decisions. The purpose of this study is to gain a deeper understanding of how visiting behaviors in online communities affect consumers’ offline purchasing behavior. The moderating role of two dimensions of consumer visiting behaviors (visiting depth and visiting breadth) also receives attention. Moreover, the impact of consumer visiting behaviors on offline sales for different types of online communities is also the focus of this paper.Design/methodology/approachTo test the empirical model, the authors collected data on consumers’ visits to an online real estate platform with local housing sales data. In addition to the baseline regression analysis of the data with the help of Stata 17.0, the study also analyzes the robustness of the results through several methods.FindingsThe authors focus on an online community for newly-built housing and find that consumer visits to the focal online community have a positive impact on offline sales. Visiting breadth has a negative moderating effect on this relationship, and no statistically significant moderating effect is found for visiting depth. Further, our empirical exploration finds that consumer visits to competitive online communities have a positive impact on offline focal product sales, but visits to complementary online communities have no statistically significant effect on offline sales.Originality/valueOur findings contribute to the understanding of consumers’ cross-channel purchasing behavior and provide new insights into how visiting behaviors in online communities affect consumers’ purchasing decisions.