Real Estate Property Research Articles

Mortgage – a solution for property buyers

The purpose of this article is to present mortgages as a solution for real estate buyers within the real estate market, providing an understanding of how mortgages work, the advantages and risks involved, as well as their impact on the real estate market as a whole. The article focuses on the relevance of mortgage loans as a solution for individuals intending to purchase real estate properties by outlining and detailing the essential stages that interested individuals go through. Additionally, the current situation of the mortgage credit market is analyzed, both on a national and international level, through the lens of key indicators associated with mortgage loans, while also examining expert opinions on market trends.

Modelling Prediction of Cities Real Estate Price Trend Using Recurrent Neural Network: A Case of Dar es Salaam City

Real estate refers to a class of real property such as land and its associated infrastructure. The prediction of real estate prices in cities, which is affected by a number of parameters, is an open research problem. The lack of reliable and effective tools for price forecasting in real estate, especially in residential housing, can adversely affect investment flows and the growth of the real estate sector. Taking Tanzania as an example, the price prediction practices rely on human suggestions that are prone to personal bias and subjective to price hysteria for personal gain and impact consumer expectations. To address the challenge, this paper designed a real estate price trend prediction model for the cities using Recurrent Neural Networks (RNN) with a Long Short-Term Memory (LSTM). The study identified the factors influencing real estate property prices, including size, location, time, property quality, accompanied services, market nature, price of land, cost of building materials, and value for money. However, the study spotted the size, price, location, and time as key factors in predicting price trends when using RNN-LSTM. The results show that the proposed RNN-LSTM model performed better with 50% MSE less compared to the Convolutional Neural Network (CNN). In computing the price trend per location, the model prediction accuracy was 97.45%, 79.23%, and 53.8% for the high class, middle class, and low class, respectively, resulting in an average prediction accuracy of 76.8%

The main aspects of geodetic surveys establishing the boundaries of a municipal road

The article discusses the structure that facilitates the registration of a real estate property in the State Real Estate Cadester (SREC). The importance and significance of carrying out engineering surveys in relation to real estate objects are reflected in this paper. The authors found the points of the state geodetic network (SGN), visited each SGN point and approved their coordinates. To study the road network, the authors chose the method of satellite geodetic determinations in fast static mode. The selected equipment - EFT M1 GNSS receiver - collects the most accurate data using the advanced technology and the highest levels of performance, precision and quality. The cadastral engineer processed the obtained coordinates and formed a geodetic construction scheme. In conclusion, the technical plan for putting the facility into operation was prepared on the basis of the received data.

Outsourcing Practices on Customer Satisfaction in Public Residential Real Estate in Mombasa County, Kenya.

In today’s ever-competitive business environment, real estate organizations constantly look for ways to improve their services and create value for their customers. Most organizations outsource managerial functions to external experts to optimize service delivery. Outsourcing provides access to specialized competence and technology, which reduces operational costs for the organization. Earlier studies on outsourcing have focused mainly on its impact on organizational performance and profitability. This study examined the influence of outsourcing practices on customer satisfaction in public residential real estate in Mombasa County. The study employed a descriptive design and a quantitative research method to answer the research problem. The target population included all the public residential properties in Mombasa County. The study adopted a stratified sampling technique to get a representative sample from three parastatals, including Kenya Ports Authority, Kenya Railways, and Kenya Power and Lighting Company, that own and manage public residential real estate in Mombasa County. From a target sample of 200 participants, 150 participated in the data collection process, resulting in a 75% return rate. The study relied on primary data collected from the field using self-administered standard structured questionnaires with closed-ended questions designed to help address the research questions. The collected data was analyzed quantitatively using Statistical Package for Social Sciences (SPSS) version 24, and the outcome was presented using descriptive and inferential statistics. On the factors that drive organizations to outsource services, the findings revealed that the organizations outsourced to enhance the efficiency of their property management and ensure the quality-of-service delivery through access to affordable skilled labor, better sanitation, and improved performance. A multiple regression analysis was conducted to determine the association between customer satisfaction (Dependent variable) and outsourcing practices (administrative services, financial service outsourcing, human resource, and sanitation services outsourcing). The regression analysis found that outsourcing practices of three independent variables (administrative, financial, and sanitation) significantly influence customer satisfaction in public residential properties since their p-values (p < .05) were less than the significance level of 95%. The findings showed that the regression model adopted in the study was significant in predicting the influence of outsourcing practices on customer satisfaction. Based on the results, the study concluded that embracing outsourcing practices in public residential properties enhances customer satisfaction by augmenting operational efficiency and improving the quality-of-service delivery. Therefore, the findings provide critical information that can help the parastatals enhance their outsourcing competencies to improve customer experience in their real estate properties in Mombasa County. Improved service provision would lead to customer satisfaction; however, organizations should consider the potential challenges of outsourcing.

Analysis of uncertainty and variation in underground train-induced vibration based on measured data

Ground-borne noise and vibration from metro trains can cause human distress/annoyance, affect the growth of animals and plants, and also negatively affect real estate property values. Before construction, the common prediction methods for the environmental vibration caused by metro trains are physical model prediction and analogy test. However, the rationality of the analogy test and the factors that need to be considered in the simulation prediction have not been systematically tested and studied. In order to solve this problem and provide experimental reference for the prediction model, one of China’s subway were systematically tested to investigate wheel roughness, rail roughness, train running speed, axle load changes, the vertical and lateral contributions, and the influence of random factors existing in the test on the vibration of subway operating environment, the following conclusions are obtained: (1) The test state randomness (random factors in the test, such as wheel-rail contact points, roughness phase, etc.) mainly affect frequencies below 31.5 Hz segment, mainly from the randomness of the wheel roughness phase or the variation of the spatial position of different contact points during actual operation, which result in large different of long-wave roughness of the wheel and rail; (2) The wheel-rail lateral force also has great influence on the environmental vibration. When establishing the environmental vibration prediction model, it is necessary to consider the state that the train reaches the straight line after passing through the curve, and the influence of track lateral irregularity; (3) The impact of wheel roughness on the environmental vibration is very large. Basically, it has an impact on the whole evaluation frequency band of environmental vibration.

Financialization of Real Estate Assets: A Comprehensive Approach to Investment Portfolios through a Gender-Based Study

Financialization is a process within the global economy wherein financial markets have been gaining ground in recent years. And yet, whether it has a beneficial or detrimental impact is almost totally unstudied, notably in terms of market effects on the real estate sector. To probe more deeply into such a research issue, this study addresses how real estate’s financialization impacts asset distribution by analyzing investment attitudes based on verifiable data from the Spanish wealth tax return. Despite scarce previous literature in the field, the study focused on the gender aspect to highlight the importance of investment decisions concerning risk aversion and related issues through the empirical analysis of the investment portfolios of those taxpayers subject to wealth tax statements. The findings identified show that financialization can lead to the conversion of property assets into stock-related movable assets, and from more to less imbalance, respectively, in terms of the gender gap. The results obtained show a significant gendered difference concerning investment stocks, being thus out of alignment with the gender equity traditionally promoted by the Spanish government. This suggests that although most investment portfolios of real estate properties are based strictly on criteria of profitability, female investors need to be empowered because they could provide a better approach to an economic issue wherein aversion to risk can become a proper criterion of private investors, even when there are sudden macroeconomic changes.

The Effect of Growth Opportunity, Net Working Capital and Leverage on Cash Holding

The purpose of this study is to determine the effect of Growth Opportunity, Net Working Capital, and Leverage on Cash Holding in the Real Estate Property Sector Listed on the Indonesia Stock Exchange (IDX). Companies in the property and real estate sector listed on the Indonesia Stock Exchange from 2017 to 2021 are the study population. In this study, 180 samples from 36 companies were sampled using purposive sampling method and observed for 5 years. In this study, this type of research used a quantitative approach using multiple linear regression analysis techniques using the e-views application 9. The results showed that simultaneously growth opportunity, net working capital and leverage have an effect on cash holdings. Partially, growth opportunity has no effect on cash holding, while net working capital and leverage have an effect on cash holding

Real Estates Developing Situation after 21 Century and FuturePredictions Towards Its Possible Trend

With the emergence of real estate aiming for commercial usage, the Chinese economy had built an unbreakable chain with the real estate market. Real estate is indeed a subject that needs to be researched but still doesnt draw enough attention from the academic circle. This paper analyzes the changes of real estate as a property accompanying the national economy's indexed changes and makes predictions of their future growth trends in order to give some suggestions to people who would like to purchase China Mainland houses, buildings, apartments, etc. The author wrote this paper mainly by collecting information and statistical information from JOURNAL OF SHANGHAI ECONOMIC MANAGEMENT CADRE COLLEGE, a research report from business school, Guangxi University, papers from The 15th meeting of the National Symposium on the theory and practice of socialist economy in Colleges and Universities and so on. The conclusion of this paper is that real estate in China will experience an appreciation in at least the next 10 years, This paper also gives recommendations for purchasing real estate properties instead of saving cash in the banks to not only China residents but also investors all over the world.

Prediction of Apartment Price Considering Socio Economic and Crime Rates Factors in DKI Jakarta

Investing in real estate properties in Indonesia is highly lucrative due to their consistent appreciation in value. Amongst the various property types, apartments are particularly favored for investment in limited land space. However, determining the value of apartments is often subjective and lacks quantitative measures. To address this issue, this study develops prediction models to predict rental prices and asset value based on apartment specifications, socio-economic factors, and crime rates. Machine learning models employed include Random Forest, Decision Tree, and Gradient Boosting Machine. The findings show Gradient Boosting Machine exhibits the highest accuracy in predicting apartment rental and sale prices, achieving R² values of 0.9230 and 0.8460, respectively. The study also highlights the significant influence of socio-economic factors and crime rates on the performance of the models, contributing between 0.09 and 0.22 with an average of 0.14, as indicated by the improved R² values. This study demonstrate that these models can be valuable tools for real estate investors and professionals seeking quantitative measures to determine the value of apartments. By incorporating apartment specifications, socio-economic factors, and crime rates, the models can provide objective insights into the potential rental income and asset value of apartments.

(Invited) The Importance of Patent Claim Language on the Path from Invention to Product

Patents often offer significant competitive advantage for inventions on the path from discovery to product. Specifically, the inventors/owners of a patent are given the right to prevent other parties from making, using or selling the subject invention. The claims of the patent define the scope of competitive advantage for the subject invention. Regarding patents,[1] “...the name of the game is the claim.” The wording of claims has been called,[2] “...one of the most bizarre sentence structures in the English language.” And, the courts have noted that,[3] “...the nature of language makes it impossible to capture the essence of a thing[invention] in a patent application[claim].” Consequently, electrochemical scientists, engineers and technologists involved in transitioning discoveries to product are well advised to understand the meaning and scope of the claims in their patented inventions.In this presentation, we will compare and contrast claims as analogous to real estate property. We will provide a brief history of U.S. patent law in terms of the requirements of claims in a patent application. We will review claim construction, i.e., establishing the meaning and scope of an invention, beginning with the inventor internal Invention Disclosure, the preparation of the patent application with a patent attorney, and the examination of the patent application by the U.S. Patent & Trademark Office. Once the patent application is allowed and issues as a patent, the meaning and scope of the claim is settled, unless the patent becomes the subject of a patent validity or infringement challenge.During patent infringement challenges, the meaning and scope of the patent claims are determined in order to decide if the patent is valid and/or if infringement has occurred. We will review the rationale and evolution of claim construction from a factual inquiry by juries to a matter of law determined by the courts. We will present the hierarchy of factors used by the courts during claim construction.Finally, we will conclude the presentation with a brief review of claim construction during a lithium battery infringement case.[4] We conclude the inventor can play a significant role in claim construction with the initial preparation of the internal Invention Disclosure, the preparation of the patent application with a patent professional, and during the examination of the patent application by the U.S. Patent & Trademark Office. Acknowledgements: The financial support of Faraday Technology, Inc. corporate R&D is gratefully acknowledged.

Who owns France? Uncovering the structure of property ownership for a better understanding of the socio-spatial distribution of wealth

ABSTRACT This paper investigates the structure of French property ownership and shows how it is crucial input to understand the socio-spatial distribution of wealth and inequalities in cities. To date only partial information exists on this given that we are in a period of financial opacity. In this paper, we provide a theoretical framework to analyze the structure of property ownership. It is defined by four dimensions: the distribution of properties among different categories of owners, the spatial patterns of the properties held by these categories, their estimated financial value, and their concentration within the categories—the latter not studied here, for legal and confidentiality reasons. Drawing on unpublished cadastral data on property owners and property transaction prices, we carry out an empirical analysis which reveals the dominance of household and public ownership, contrasting with the marginal position of private investors. Despite the fact that the structure of ownership is fairly uniform throughout the urban hierarchy, some groups of owners do however hold strategically located properties. Our results show the embeddedness of the property ownership in different accumulation regimes, and relativize any consideration of financialization or neoliberalism as a global all-encompassing framework when analyzing real estate property.

The Enhancement of Special-Use Real Estate Properties: The Case of Hospital Facilities

In the Italian context, public investments for the redevelopment and securing of the National Health Service’s real estate assets are a crucial topic in the context of the National Recovery and Resilience Plan (NRRP) within the Next Generation Italian strategy. The paper proposes the evaluation of alternative scenarios for accessing financing under the NRRP with respect to the criterion of the technically efficient solution, i.e., the solution that minimizes investment costs while respecting time obligations. The methodology proposed refers to the Cost approach with specific reference to the Depreciated Replacement Cost Method (DRC) in order to estimate the market value in different scenarios. The approach is applied to a case study located in the Piedmont Region, where alternatives are compared with respect to both budget constraints and the timeframe for accessing financing. Given the growing concern for urban regeneration and “public city” rearrangement as an answer to the ongoing global changes, making investments in special-use real estate properties has become a central and challenging issue both in the public and private decision domains.

Sea Level Rise and Future Projections in the Baltic Sea

This article aimed to provide an overview of relative and absolute sea level rise in the Baltic Sea based on different studies, where researchers have used data from tide gauges, satellite altimetry, sea level rise, and land uplift models. These results provide an opportunity to get an overview of the sea level rise in the Baltic Sea. However, to better understand the impact of sea level rise on the coastal area of the Baltic Sea, and especially in Estonia, two post-glacial land uplift models, the latest land uplift model NKG2016LU of the Nordic Commission of Geodesy (NKG) and Estonian land uplift model EST2020VEL, were used. These models enabled to eliminate post-glacial land uplift from absolute sea level rise. To determine the relative sea level rise in the coastal area of the Baltic Sea, the rates from land uplift models were compared to ESA’s BalticSEAL absolute sea level rise model. It was found that the relative sea level rise between 1995–2019 was −5 to 4.5 mm/yr (based on NKG2016LU) in the Baltic Sea. In addition, the southern area is more affected by relative sea level rise than the northern part. During the research, it was also found that the IPCC AR5 sea level projections predict a maximum relative sea level rise in the Baltic Sea by the year 2100 of between 0.3 to 0.7 m. As coastal areas in the southern part of the Baltic Sea are predominantly flat, the sea level may reach the real estate properties by the end of the 21st century. In the coastal area of Estonia, the relative sea level rise in the period 1995–2019 was −1.1 to 3.1 mm/yr (based on NKG2016LU) and −0.3 to 3.4 mm/yr (based on EST2020VEL), the difference between the land uplift models is −0.9 to 0.1 mm/y. In Estonia, the west and southwest area are most threatened by sea level rise, where the coast is quite flat. One of the largest cities in Estonia, Pärnu, is also located there. Using the ESA’s sea level and EST2020VEL land uplift models, it was found that the relative sea level rise will be 0.28 m by the year 2100. Based on the large spatial resolution IPCC AR5 sea level projections, the relative sea level rise will be on the same scale: 0.2–0.4 m.

Consumer Preferences for Residential Real Estate Properties among Interested Buyers in Laguna Province, Philippines, before and during the COVID-19 Pandemic

Purpose – This study assessed the consumer preferences for residential real estate properties among interested buyers in Laguna, Philippines, before and during the COVID-19 pandemic. Design/Methodology/Approach – Interested property buyers in Laguna were randomly selected and characterized based on their socio-demographic and economic characteristics, and their COVID-19 experiences were described. Consumer preferences for different aspects of housing units were examined descriptively and using McNemar’s test, changes in preferred attributes of residential units before and during the pandemic were compared. Findings – Single-detached economic housing with three bedrooms was the most preferred type of unit of interested buyers. It was found that the higher the monthly income of respondents, the higher the property value they preferred to purchase. Los Baños is the preferred municipality to live in because of its proximity to the metropolitan capital. The preferred mechanism of ownership is either rent-to-own or via a loan. Those earning a monthly income of PhP 131,484 (USD 2,708.77) to PhP 219,140 (USD 4,514.63) preferred to purchase residential property in cash. As for attributes, proximity to highways, proximity to community services, proximity to houses of family and friends, presence of swimming pool, presence of sense of community, secured and guarded community, quiet surroundings, presence of panoramic views, and pet-friendly home and community are statistically significantly different. Research Implications – Residential real estate developers should respond to various differences in consumer preferences. To continue satisfying prospective buyer demand and successfully close sales, it is vital to consider these consumer preference changes relative to residential projects.

AUGMENTED AND VIRTUAL REALITIES: THE FUTURE OF BUILDING DESIGN AND VISUALIZATION

The present study precisely conveys the methodology of developing a three-dimensional (3D) architectural model of a villa with its walk-through and displaying the model in virtual reality, which as a result, be used by the clients to spectate, customize and buy the real estate property. Additionally, the case study highlights the advancement in architecture, as certain specifications of each element of a 3D model can be viewed in a virtual environment. Virtual reality is a transpiring platform, and in addition to that, the real-estate sector shows its incorporation in designing, marketing, and selling projects. The teaching and learning process can be eased out by intervening it with technology that generates an enhanced visualization environment. These technologies, when used constructively, save time and energy and also hoard economic standards ensuing lucrative benefits.

An Empirical Analysis of Non-Immersive Virtual Reality on Behavioral Intentions: Moderating Role of Openness to Experience

This study is used to examine the impact of virtual reality in real estate and the effects of offeringconsumers non - immersive Virtual reality (VR) experience through broadlyexisting technology, to allow them to viewreal estate products. This research also highlights such technologies to enhance the customer’s experience, thoughts, and attitudes generally in a vastly competitive real estate industry. Data have been collected from the consumer of thereal estate sector. 302 consumers filledout the questionnaires and finally, valid responses 206 have been used for the PLS Structural Equation Modeling (SEM) analysis. The systematic results discovered better "visiting" experiences and more positive attitudes toward both the products and the agency. Further, results postulate that knowledge of properties mediates the positive relationship between the visiting real estate properties through VR and behavioral intention. Furthermore,Openness to experience moderates the positive effect between visiting real estate properties and behavioral intention. This studyprovidesa valuable implication on a realistic level, this study also stressed the importance of offering consumers an enhanced experience of promoted products for practitioners to positively impact consumer's attitudes and behavioral intentions.

The Role of Thermodynamic and Informational Entropy in Improving Real Estate Valuation Methods.

Price, Cost and Income (PCI) are distinct economic indicators intrinsically linked to the values they denote. These observables take center stage in the multi-criteria decision-making process that enables economic agents to convey subjective utilities of market-exchanged commodities objectively. The valuation of these commodities heavily relies on PCI-based empirical observables and their supported methodologies. This valuation measure's accuracy is critical, as it influences subsequent decisions within the market chain. However, measurement errors often arise due to inherent uncertainties in the value state, impacting economic agents' wealth, particularly when trading significant commodities such as real estate properties. This paper addresses this issue by incorporating entropy measurements into real estate valuation. This mathematical technique adjusts and integrates triadic PCI estimates, improving the final stage of appraisal systems where definitive value decisions are crucial. Employing entropy within the appraisal system can also aid market agents in devising informed production/trading strategies for optimal returns. The results from our practical demonstration indicate promising implications. The entropy's integration with PCI estimates significantly improved the value measurement's precision and reduced economic decision-making errors.

Image Based Appraisal Real Estate Properties

Abstract: Real estate appraisal, which is the process of estimating the price for real estate parcels, is vital for both deals and merchandisers as the base for concession and trade. Traditionally, the duplication deals model has been vastly espoused to estimate real estate price. still, it depends the design and computation of a complex profitable affiliated indicator, which is challenging to estimate directly. moment, real estate brokers give easy access to detailed online information on real estate parcels to their guests. We're interested in estimating the real estate price from these large quantities of fluently entered data. In particular, we dissect the vaticination power of online house cinema, which is one of the vital factors for online addicts to make a implicit visiting decision. In this work, we employ a intermittent Neural Network( RNN) to prognosticate real estate price using the state- of- the- art visual features. The experimental results indicate that our model outperforms several of other state- of- theart birth algorithms in terms of both mean absolute error( MAE) and mean absolute chance error( MAPE).

The Influence of TIF Overlay Zoning on Residential Real Estate Prices

Tax increment financing, a form of overlay zoning, are purported to spur economic development and promote the public interest of the community. However, these municipal overlay zones frequently generate hostility due to their use of public funds. While a large volume of knowledge exists on the impact of residential real estate prices within a tax increment financing district, little is known about the impact of the municipal overlay zone on the surrounding community. Using four, similarly timed, but separate tax increment financing districts and a transactional residential real estate dataset, we test price implications for residential real estate property at three separate geographic distances. Using propensity score matching and fixed effects modeling, we find that the imposition of tax increment financing districts increased surrounding residential real estate property values.

An Algorithm to Predict Real Estate Price using Machine Learning

Real Estate Pricing has always been a varying from time to time keeping a buyer confuse on how to get exact price of the property at certain point of time. A machine learning algorithm is used to predict the exact price of the property. Predicting the accurate amount of the real estate is very much a matter of concern as people are investing too much now a days in property where a property dealer could easily charge more amount if the buyer is not knowing the market price of the property. The major focus of this research paper is to predict the accurate price of the property without a hassle. Apart from that it also focuses on increasing the accuracy of the already existing system. There is various machine learning algorithm available for prediction such as Naive Bayes, Logistic Regression, Classification, Random Forest KNN, Support Vector Machine, Lasso, Linear Regression etc. The aim of this research is to predict the market value of real estate properties based on geological location. By analyzing previous market patterns, value ranges, and upcoming developments, we can determine a starting price for a property based on geological variables. Our study is applicable to any location, and we have utilized three machine learning algorithms to make predictions. Our findings indicate that linear regression provides the most accurate predictions, with an accuracy rate of 85%. This system eliminates the need for clients to rely on brokers and provides them with the confidence to invest in real estate. The accuracy of our system surpasses that of previous methods used in the industry.