Real Estate Costs Research Articles

HOUSING PRICES AS PAYMENT FOR ECOSYSTEM SERVICES OF URBAN GREEN SPACES: CASE STUDY IN KYIV

The purpose of the study is to analyze the impact of the availability of urban green spaces (UGS) and air quality on the cost of residential real estate in Kyiv. Additionally, it seeks to identify the level of awareness among city residents regarding the advantages of living close to UGSs in areas with relatively clean air and their willingness to pay a higher price for such housing. The QGIS software was employed to assess the pedestrian and time accessibility levels of recreational green spaces, and the results are depicted on the corresponding map. The least accessible for everyday recreation is the UGS in the central districts of the city, which traditionally have the highest population density and the most expensive residential real estate. The article analyzes the priority criteria for determining the housing cost, depending on the spatial localization relative to the city center and the comfort class of real estate. The distribution of housing across different tiers and the price range for the apartments in new constructions in Kyiv during 2015–2021 were also examined. To identify the correlation between the cost of residential real estate in the capital’s new buildings and the availability of green spaces nearby, we investigated the spatial distribution of prices in the largest residential complexes in the city. The results of spatial visualization obtained through Voronoi diagrams or Delaunay triangulation indicate significant disparities in the cost of housing located in the center of Kyiv or on the periphery. Logically, residential complexes with the highest apartment prices are located in the central districts of Kyiv and near the metro stations. However, most of these locations lack accessible green areas suitable for everyday recreation. Therefore, there is no positive correlation between housing prices and the presence of UGSs nearby. To achieve the research goal, we also conducted a sociological survey among Kyiv residents in various districts of the city. The survey results indicate that most Kyiv residents do not have the desire or are not currently willing to pay a higher price for the opportunity to live near a park or forest and breathe clean air, i.e., to enjoy the ecosystem services provided by UGSs fully. The scientific novelty of the research lies in determining the pedestrian and time accessibility of recreational green spaces in Kyiv, identifying trends in spatial clustering of the most expensive residential real estate in central city areas, and demonstrating the lack of a positive impact of the availability of green areas on the per square meter cost of housing in new developments. Additionally, the study reveals a remarkably low willingness of the city residents to pay a higher price for the opportunity to receive ecosystem services from available UGSs.

IMPACT OF SOME FACTORS ON PRICING IN THE REAL ESTATE MARKET OF RUSSIAN MEGACITIES

The authors analyze pricing in the residential real estate market in Moscow and St. Petersburg – the largest cities of the Russian Federation in the article. The issue of the cost of residential real estate is one of the most important for the residents of Russian megapolises. Megapolicies are the largest centers of migration, both from other regions and from other countries. The population of such cities is expanding at a significant rate, and the construction industry is increasing the rate of creation of new real estate objects. Under such conditions, it is crucial to realize the dependence of real estate value and key externalities.Within the framework of the study, the authors rely on the works in the field of real estate valuation and statistical methodology. The group of authors puts forward statistical hypotheses about the impact of some factors, in particular the level of cumulative inflation, the key rate set by the Central Bank of the Russian Federation, the average level of wages in the region and the weighted average rate on mortgage loans to citizens on the formation of the weighted average cost per square meter of housing in Russian cities.The statistical method of factor analysis is used to assess the significance of each of the factor variables on the resulting parameter. Regression modeling allows estimating the degree of impact of significant factors on the cost per square meter of residential real estate. The models allow to assess the trends of changes in the cost of real estate on the primary and secondary residential real estate markets in Russian megacities, as well as provide an opportunity to consider the differentiation in the impact of each factor between different types of markets in different cities.The results of the study may be useful in building industry forecasts and developing government measures to support residential construction.

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

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

Organisations’ and employees’ perceptions of the impact of activity-based working on the organisational culture

Purpose Challenged by the effects of organisational flexibility and high corporate real estate (CRE) costs, organisations increasingly seek flexibility and operational efficiency in their physical office layouts. This paper examines and compares how the existing organisational culture of organisations changed with the introduction of activity-based working (ABW) from the perspectives of organisations and their employees. Design/methodology/approach The research focused on large organisations that had introduced ABW during the period of 2012–2019 and their employees. Two surveys were implemented with the management of organisations and employees. In-depth interviews with the management were also undertaken. The research focused on three different industry sectors in Australia – financial, IT and public. The competing value framework was used to measure the dimensions of organisational culture. Findings Findings identify major finance, internal business, learning and growth and staff and client-oriented motives affecting organisations’ decisions to introduce ABW. Findings illustrate that the nature of workplace design has a considerable impact on organisations’ culture and can be used to leverage and change it. However, a noticeable discrepancy between the perceptions of the organisation and employees in the public and private sectors was identified, where public sector employees felt that standardised procedures still governed their actions even in ABW. Originality/value This research highlights that workplace designs directly influence the culture of organisations. It emphasises the importance of an in-depth examination of the behavioural and attitudinal characteristics of organisations and their employees to obtain a better understanding of how they perceive and interact in ABW layouts.

A RESEARCH ON CLOUD COMPUTING

Cloud computing is an important technology for the development of information technology business. It is one of the best technologies for managing and distributing large amounts of information and resources over the Internet. In general terms, cloud computing refers to accessing IT infrastructure over a computer network without installing anything on a personal computer. Businesses can use cloud computing to tailor their resources to meet their operational needs. Organizations and businesses can reduce real estate costs by using the cloud. Organizations can test their applications faster with better control and less maintenance. Thanks to cloud computing, IT teams can adapt to changing resources and changing needs. There is evidence that cloud computing plays an important role in daily life due to its many applications in different environments. This article will cover all aspects of cloud computing including its architecture, features, types, service models, advantages and challenges.

Research on the Price Prediction of Commercial Housing in Beijing

In order to determine the best appropriate housing price prediction model, this study constructs a linear regression model, a BP neural network model, and a time series model based on data on commercial housing prices and their affecting variables in Beijing during the past 23 years. A set of 12 independent factors that impact the cost of commercial housing is selected first. Then, three models are created, and their fitting effects are compared and analyzed to forecast the average cost of commercial real estate in Shandong Province. It is shown that the multiple linear regression model is more appropriate for long-term prediction and that the time series model predicts data more recently than the BP neural network model; Therefore, it can be used if the prediction period is longer. Although home prices can be predicted theoretically using these three approaches, more work has to be done to fine-tune the applicable models.

The Main Problems of the Real Estate Market Functioning in the Republic of Korea

The article examines the problems of the functioning of the real estate market of this country on the basis of official data of the Statistical Bureau, the Civil and Housing Codes of the Republic of Korea. The paper considers the features of state regulation of ownership of real estate. The calculation of the level of urbanization and the coefficient of specialization according to the level of development of the real estate market of the regions of the Republic of Korea is carried out. The indicators characterizing the availability of real estate are presented, as well as the rating of regions by the level of development of the real estate market. The main problems include: the high cost of real estate, the low percentage of the population owning real estate, large regional differences in the level of development of the real estate market, identified regions where the purchase or rental of real estate is disproportionate to the income received. The article includes author’s maps illustrating the results of the study.

Working from home: Too much of a good thing?

We develop a general equilibrium model with skilled workers who can and unskilled workers who cannot work from home (WFH). Firms choose the amount of time they require workers in the office, whereas workers choose to either work on-site or hybrid, splitting working time between office and home. The endogenous work arrangements determine productivity, wages, and demand for residential and commercial real estate. We find that firms ‘outsource’ workers to their homes to save on real estate costs, and in doing so push beyond the WFH share that maximizes skilled workers’ productivity. This effect is more pronounced if land-use regulations are strict, thus showing another channel through which the latter may reduce productivity. More efficient information and telecommunication technologies allow firms to shift office expenditures toward skilled workers who invest more in home working space. In a nutshell, WFH may well be the ‘new margin of offshoring’ for firms.

Phasing real estate projects considering profitability and customer satisfaction

PurposeThe aggressive competition in the real estate market forces real estate developers to tackle the challenge of selecting the best project construction phasing alternative. The real estate industry is characterized by high costs, high profit and high risks. The schedules of real estate projects are also characterized by having large number of repetitive activities that are executed over a long duration. The repetitiveness, long duration of execution, the high amounts of money involved and the high risk made it desirable to leverage the impact of changes in phasing plans on net present value of amounts incurred and received over the long execution and selling duration. This also changes the project progress, and delivery time as well as their respective impact on customer degree of satisfaction. This research addresses the problem of selecting the best phasing alternative for real estate development projects while maximizing customer satisfaction and project profit.Design/methodology/approachThe research proposes a model that generates all construction phasing alternatives and performs decision-making to rank all possible phasing alternatives. The proposed model consists of five modules: (1) Phasing Sequencing module, (2) Customer Satisfaction module, (3) Cash-In calculation module, (4) Cost Estimation module and (5) Decision-making module. A case study was presented to demonstrate the practicality of the model.FindingsThe proposed model satisfies the real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model generates all construction phasing alternatives and performs multi-criteria decision making to rank all possible phasing alternatives. It quantifies the score of the two previously mentioned criteria and ranks all solutions according to their overall score.Research limitations/implicationsThe research proposes a model that assist real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model can be used to conclude general guidelines and common successful practices to be used by real estate developers when deciding the construction phasing plan. In this study the model is based on business models where all the project units are sold, rental cases are not considered. Also, the budget limitations that might exist when phasing is not considered in the model computations.Originality/valueThe model can be used as a complete platform that can hold all real estate project data, process revenues and cost information for estimating profit, plotting cash flow profiles, quantifying the degree of customer satisfaction attributable to each phasing alternative and providing recommendation showing the best one. The model can be used to conclude general guidelines and common successful practices to be used by real estate developers when tackling the challenge of selecting construction phasing plans.

Application of methods based on ensembles and deep neural networks to estimating the cost of commercial real estate

The paper considers the possibility of using ensemble machine learning models and artificial neural networks to solve the problem of assessing the value of commercial real estate. There are some models such as the gradient boosting model and the TabNet model have been trained. The main goal of these models is predict the value of commercial real estate without creating dependencies between data by the analyst. The proposed solutions are considered from the point of view of the banking sector. The best predictive model is the gradient boosting model implemented using the LightGBM library. The advantages of this model are associated with its ability to "resist" the presence of outliers in the data and a low propensity for retraining.

텍스트마이닝을 활용한 부동산 시장 인식 변화 연구

This study compared and analyzed real estate market trends over the last six years using social big data generated on the web and social media. To this end, the applicability of social network analysis as a method of confirming and understanding public perception of the real estate market was confirmed. Big data were used for analysis, and because keyword network analysis was at the heart of the study, the author's patented “emotional information analysis method” was partially used. The analysis result reveals that from 2017 to 2019, keywords related to the rising phase of the real estate market were derived with high influence, and real estate policy keywords were ranked highly. Meanwhile, from 2020 to 2022, keywords related to the real estate market downturn had a high impact, and those related to real estate costs were ranked high. These research findings are a preliminary study that seeks an effective research methodology in understanding the past and present real estate markets. More specific policy research should be conducted using these research results.

Региональные особенности рынка недвижимости Приморского края

The regional features of the real estate market of Primorsky Kray are considered. Diagrams showing research on the real estate market of the Primorsky Territory by segment are illustrated: methods of purchasing housing by consumers, the choice of type of housing, the degree of attractiveness of qualities when buying an apartment in a new building, criteria that influence the choice of developers by respondents. The results of the study are presented in the form of an online questionnaire conducted on the territory of Primorsky Kray in 2023, on the basis of which important trends in the real estate market of Primorsky Kray and features of its promotion were identified: high advertising activity is manifested in social networks and on the Internet; a decrease in the basic cost of real estate as a tool for attracting attention has reduced its effectiveness; the location of the region it affects consumer preferences in this market segment. The results of the survey and the study of the real estate market substantiate the relevance of the research, the conclusions and subsequent recommendations. These recommendations are designed for real estate agencies and to attract and retain customers of this segment, which include using unique offers for residents of the region, improving the environmental environment around real estate, using online advertising, video demonstration of real estate in real time, working with customer reviews, participating in real estate-related events, holding contests on their Internet resources and the promotion of real estate with an emphasis on its location.

ALGORITHMS FOR OBTAINING A RESIDENCE PERMIT AND PURCHASING REAL ESTATE FOR UKRAINIANS UNDER SPECIAL MARTIAL LAW CONDITIONS

Approximately a quarter of the surveyed Ukrainian refugees and IDPs said that they plan to stay in their new places of residence, in the countries of their choice, obtain a residence permit, and purchase real estate. The legislation of Ukraine, like that of other countries, provides that ownership and other real rights to real estate, encumbrances, as well as their creation, transfer and termination, are subject to state registration. Rights to real estate subject to state registration arise from the moment of such registration. In essence, state registration is the fact of public recognition by the state of a person's right to real estate by entering information about it into the State Register of Real Property Rights. However, each country has its own regulatory requirements. Therefore, research into real estate registration procedures and algorithms for obtaining a residence permit is relevant. The study is aimed at identifying the peculiarities and algorithms for obtaining a permanent or temporary residence permit by investing in real estate or business in the country of study. real estate registration for Ukrainian citizens who went abroad during the military operations and intend not to return home but to integrate into other countries. The research objects are 9 countries of the world, namely: Turkey, Greece, Montenegro, the United Kingdom, Cyprus, the United Arab Emirates, the United States, Portugal, and Hungary. These countries are characterized by: attractiveness in terms of residence, democratic development, improvement of the quality of service provision and strengthening of trust in the government by the population, the public and the private sector. The information base of the study was based on collections of scientific papers, periodicals, and Internet resources. Summarizing the results of the study, we have constructed a diagram of the cost of investment real estate for permanent residence for the selected countries. The largest investment is required in the UK, while Montenegro has no requirements for real estate investment at all. 
 It is determined that the procedure for obtaining the right to temporary or permanent residence through investment and registration of real estate may vary, and the package of documents required for registration also differs depending on the specific object and the circumstances of the rights to it. However, there are simple general recommendations that can help protect real estate rights and save time and money: compliance with the country's legislation; creation and proper operation of a unified registration system. This analysis will help to identify ways to implement foreign experience in Ukraine's activities in the future. The practical content is indicated by the target orientation of this study for Ukrainian citizens who are forced to stay abroad, urban planning and cadastre specialists.

Digital Nomads' Willingness to Stay – a Combined Analysis of SEM and fsQCA Results

Lisbon is a multicultural city with a business center that hosts major technology companies and professionals in the field, including digital nomads. High demand for tourist accommodation and the arrival of foreigners to work in the country have led to an imbalance between the supply and affordable cost of real estate for residents of the capital. This work aims to survey and determine whether digital nomads in Lisbon intend to settle in the city or are just passing through. To identify the combination of factors that influence digital nomads’ decision-making, we conducted a quantitative study using survey data tested through structural equation modelling and configurational analysis by fsQCA. The results show that while the decision to stay in the city is heavily influenced by community attachment, we found three configurations that reveal distinct patterns of digital nomads’ intention to stay in the city. These results suggest that policymakers need to consider that digital nomads are not a homogeneous group. This study makes an important contribution to the understanding of behavioral intentions and is, to the authors’ knowledge, the first to identify different configurations based on digital nomads’ intentions to stay in a city.

Analysis and Forecasting of Influencing Factors of Real Estate Price in Haikou City

With the start of construction of the Hainan Free Trade Port in 2018, Hainan has gradually become a new focus area. Haikou City, as the capital city of Hainan Province, has also begun to be paid more and more attention. Therefore, based on the relevant economic data of Haikou City from 2011 to 2021 and the grey correlation analysis and GM (1,1) prediction model, this paper investigates the variables that affect the cost of real estate in Haikou City and produces projections. According to the analysis, the real estate price in Haikou City is mainly affected by the number of deposits of financial institutions at the final year among the selected variables. Besides the real estate price in this area will have a relatively obvious growth in the next ten years. Considering the outcomes above, this study provides the government with reasonable control over the real estate industry by controlling the year-end deposit balance of financial institutions, an important indicator, and at the same time gives investors suggestions on long-term investment and holding of real estate-related products in Haikou City. These results can give relevant suggestions to Haikou City government and real estate investors, which also shed light on offering some suggestions for the study of new areas.

Put it in the bag: Order fulfillment with a pocket sorter system

AbstractDue to high real estate costs in urban areas, shop floor space is scarce in most brick‐and‐mortar stores. Maneuvering newly arrived merchandise through narrow aisles during shelf replenishment is time‐consuming for the sales staff and impedes customers. Therefore, many retail chains nowadays aim for store‐friendly shipments (SFS). By mirroring the layout of a store in the buildup of its dedicated shipments, the need for a zigzag movement through the store when replenishing shelves can be avoided. On the negative side, however, additional effort arises in the distribution centers. A suitable warehousing system to assemble SFS without excessive effort is a pocket (or pouch or bag) sorter, where each item is put into its separate bag. These bags, filled with items, are automatically transported while hanging from an overhead conveyor and can be sorted into any sequence before being delivered to the workstations that build SFS. This article investigates the assembly of SFS with a pocket sorter and presents scheduling procedures to enhance the efficiency of this process for a given set of store orders. We demonstrate that, despite its notorious complexity, the problem can be solved by simple decision rules with good performance. In a case study, we show that this approach can dramatically reduce the completion times of store orders, resulting in savings of more than 60% of the total working hours compared to a simple real‐world policy. Another 30% of reduction can be obtained by standardized store layouts.

Fuzzy model for assessing the impact of pricing factors on the cost of primary housing

The article presents the results of a study on the creation of a fuzzy model for assessing the impact of pricing factors on the cost of primary real estate. The aim of the study is to develop a fuzzy logic-based tool for analyzing and evaluating the complex relationships between various factors that affect value. The article covers important aspects of creating a fuzzy model, including the definition of pricing factors, linguistic variables, and membership functions. The process of forming a rule base is considered, which determines the logical connections between factors and their influence on the price of real estate. The essence of the concept of primary housing and factors influencing pricing based on data on the situation in the real estate market are considered. The results of the study can be a useful tool for developing pricing strategies and decision making in the real estate market. The model developed by the authors can be improved considering the characteristics of a particular market and available data.

Determine the Relationship between Population, Residents Income and the Price of Real Estate using Linear Regression

People are constantly looking for the perfect spot to live, but the price of a home isn't always in a reasonable range for them to afford it. For example, in Montreal, the price of a home is relatively higher, while in Northern New Brunswick, it is relatively lower. As is known to all, the population and income of people are different in each region. As a result, there could be a connection between the size of the population, the amount of money individuals make, and the cost of real estate. To help people to find the proper room to live, it is very meaningful to determine the factors that influence the price of a home. To start with, the table of population and peoples income in 18 different regions is listed. According to the table, the graphs of the relationship between population, peoples revenue and housing costs are sketched by using R studio. Finally, using linear regression, the conclusion that the more people live in a region, the higher price of a home is. Besides, the more income people have in a region, the higher price of a home is.

The great wall of debt: Real estate, political risk, and Chinese local government financing cost

Chengtou bond is the only asset with market prices that can capture the funding cost of Chinese local government debt. In contrast to the U.S. municipal bonds, Chengtou bonds are issued by private corporations but implicitly guaranteed by local and the central governments, which are reflected by novel risk characteristics—real estate GDP and political risk. One standard deviation increase in local real estate GDP (political risk) corresponds to 10 (9) basis points decrease (increase) in bond yields, respectively. However, conditional on political risk, real estate GDP actually increases bond yields, suggesting that only local governments with low political risk can enjoy the low funding costs driven by high real estate growth.

Drivers of gated community developments in Ghana: perspective of developers

PurposeGated communities continue to spread in popularity across cities around the world. Ghana has seen considerable growth in the development of gated communities over the last few years. This phenomenon manifests in the majority new residential developments and most forms of residential property advertisement in the capital city. The purpose of this paper is to explore the drivers of gated community developments in Ghana from the perspective of gated community developers.Design/methodology/approachThis paper used a qualitative research approach. Through face-to-face semi-structured interviews, data was collected from purposively selected key stakeholders in the gated community development market, including developers, managers and consultants.FindingsThe drivers identified from the developers' perspective were the demand for gated community properties, the control over and ease of management of assets and interests, the high development/production cost of real estate and speculative development of gated community properties.Originality/valueThis paper is an initial study that explores the drivers of gated community developments in Ghana from the perspective of gated community developers. This paper extends the literature on gated communities beyond the residents perspective.