Real Estate Values Research Articles

From Administrative Price to Market Value of Real Estate. The Evolution of the Valuation System in Poland

Abstract The article applies the method of historical research, using a temporal perspective dating back to 1985, to trace the evolution of market valuation principles for real estate in Poland. These principles evolved similarly to those in many other post-socialist countries, influenced by political and socio-economic transformations and the resulting list of objectives for which these values became essential. The changing legal regulations allowing for the emergence and development of a free real estate market played a decisive role in this process. It was also a period of preparing real estate valuation professionals to meet these requirements. Today, the methodology of valuation, under increasing pressure from various real estate market entities and the rapid advancement of intelligent data collection and processing technologies, is undergoing further evolution. In many countries, including Poland, lively discussions and disputes are ongoing regarding the legal authorization of statistical tools and automated valuation models in valuation practice. These possibilities are being considered particularly in the context of mass property valuations for tax purposes. The methodology involves the analysis of Polish legal provisions, foreign literature, and documents proving the gradual marketization of valuation principles.

Planning annuity (fee). Civil and tax law regulations

Adopting a new or changing the existing local development plan is most often associated with an increase in the value of real estate. When selling the property, the owner of the property is obliged to pay a special fee (planning rent) in connection with the increase in the value of the property. This fee is collected by the commune head, mayor or president and cannot be higher than 30% of the increase in the value of the property. In practice, many misunderstandings and problems arise in the operation of this fee. The main cause is an incorrectly calculated and overestimated difference in the value of the property, which leads to undue claims by the commune against the property owner. Art. 36 and 37 of the Act of 27 March 2003 on Spatial Planning and Development states that the condition for collecting a one-off fee (planning rent) is the need to simultaneously meet the following conditions: increase in the value of real estate as a result of adopting a new or changing the existing local plan, specifying in the local plan the percentage increase in the value of the property, constituting the basis for calculating the amount of planning rent or sale of real estate by the current owner or perpetual usufructuary

Spatial disparities in urban park accessibility: Integrating real-time traffic data and housing prices in Ningbo, China

The urban park accessibility is a vital measure of urban livability, indicating either the presence or absence of environmental equity. This study introduces a novel approach by integrating real-time navigation data with traditional accessibility assessments to examine the spatial distribution of parks in Ningbo, China. It evaluates service provision across different transportation modes and park tiers, revealing significant spatial disparities in accessibility. Particularly, it highlights a pronounced polarization, with some city core areas experiencing limited access. Additionally, this research establishes a clear correlation between housing prices and park accessibility, illustrating how urban greenery can influence real estate values. The findings underscore the need for policy interventions to promote equitable access to urban parks and to address potential green gentrification risks. The methodology proposed here offers valuable insights for urban planners and policymakers in scientifically assessing public resource accessibility and planning more equitable urban environments.

From fair market value to judicial market value of real estate

ABSTRACT The paper proposes a brief analysis of the main elements that, on a theoretical, normative and situational basis, affect the value of properties placed as collateral for loans, with particular reference to the value they assume in the event that they are affected by an enforced procedure instead of being subject to normal sale, in free market conditions. Starting from the classic analytical estimate of the fair market value of a real estate asset in free market conditions, the paper will quantify the value that the asset should have when the appraiser has to indicate the value from which to start the judicial auction. Considering the regulatory mechanisms in place in Italy, the paper will put in evidence how a value calculated for an execution sale of a property occurring in a foreclosure process is considerably far from its fair market value and even more from its final judicial value, considered as the amount that will be recovered at the end of the sale of the property by judicial auction. For both debtors and creditors, the significant differences between fair market, execution and judicial values become an increasingly topical issue in the face of the growing number of default and distress of loans.

Machine learning for real estate valuation: Astana, Kazakhstan case

Purpose of this research is to investigate the accuracy of machine learning models in forecasting and evaluating house prices, and to understand the key factors that impact pricing. The study involved analyzing data scraped from real estate ads in the “sale of secondary housing” category on the website krisha.kz. The paper emphasizes the importance of understanding the factors that affect house prices, such as quality, location, size, and building materials. It was concluded that these factors have a strong correlation with house price prediction. The information available on krisha.kz was found to be a useful resource for finding good apartments. The data collected by the scraper was analyzed by models: Linear regression (LR), interactions linear regression (ILR), robust linear regression (RLR), fine tree regression (FTR), medium tree regression (MTR), coarse tree regression (CTR), linear support vector machine (LSVM), quadratic SVM (QSVM), medium gaussian SVM (MGSVM), rational quadratic gaussian process regression (RQGPR), boosted trees (BoosT), bagged trees (BagT), neural network based on the bayesian regularization algorithm (BR-BPNN). BR-BPNN showed better results than other models, with an MSE of 32.14 and R of 0.9899.

Population shrinkage and its influence on housing prices: A mediation perspective based on commercial amenities

The effects of population shrinkage have become a new topic of concern. Existing research has explored the effects of population shrinkage on housing prices within specific cities, yet due to scale variations, the effects of population shrinkage on housing prices may exhibit uncertainty, are conclusions drawn at the micro level applicable to the macro level? Currently, there is limited exploration of the collective effects of micro mechanisms of population shrinkage on housing prices. In this paper, we outline the effects of population shrinkage on housing prices at both macro and micro levels. Subsequently, we propose to explore the general effects of population shrinkage on housing prices based on commercial amenities. Specifically, we analyze the spatial patterns and characteristics of population shrinkage and housing prices in 1235 Chinese shrinking counties. Furthermore, using partial least squares structural equation modeling (PLS-SEM), we examine how commercial amenities mediate the relationship between population shrinkage and housing prices. This study contributes theoretically and empirically to understanding the impact of population shrinkage on housing prices, while also holds practical significance for regulating the effects of population shrinkage and stabilizing the value of real estate markets from the perspective of commercial amenities.

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.

Forecasting the Value of Real Estate Using Machine Learning Tools

Correct valuation of real estate plays a crucial role in the process of buying and selling. We have carefully studied the existing applications with which we carry out real estate transactions, described their features, advantages and disadvantages. The developed model will help sellers get an estimate of their property according to the parameters entered, which can serve as a starting point for establishing the final value. The computation of real estate values has historically been based primarily on the method of analyzing data manually and subjective estimates, often resulting in errors and delays. The use of machine learning algorithms in solving this problem turned out to be effective, since it has a number of advantages over the manual estimation method, namely: a high level of accuracy, elimination of subjectivity and bias in estimates, time efficiency, cost reduction, use of geospatial data and substantiation of results. The process of creating a machine learning model is conditionally decomposed into four stages, which include collecting data, filtering, processing, supplementing, dividing into different samples and training the model based on this data. We considered the most popular regression algorithms, briefly described the principle of their work, as well as metrics with which you can evaluate the quality of the predicted values of the models. Standard parameters were used to test linear regression algorithms, decision tree, nearest neighbor method, support vector method, and random forest. The determination coefficient R-square is chosen as the main metric. Comparing the coefficient of determination of the results, it became clear that the algorithm “random forest” showed the best result. Having manually selected hyper parameters for this algorithm, the average value of the absolute error of the predicted value is 8.49 %, and the median is 1.9 %. The constructed model meets the established quality requirements and is ready for implementation in the information system of forecasting the value of real estate. For buyers, this service will be relevant, since they will be able to search for real estate according to the parameters entered by them, which have a favorable price for the purchase.

Modeling the effect of shoreline change on real estate value

While significant global research has been dedicated to value estimation and its influencing factors, which are crucial for the real estate sector, a discernible gap exists in studies examining the long-term impact of shoreline modifications, especially those induced by upcoming coastal constructions, on coastal real estate values.The study was conducted in a coastal region of the Karasu district, Sakarya province. The COPREM-2D numerical model was used to predict shoreline change. A hedonic value model, rooted in semi-logarithmic regression, was developed, yielding value equations for residences, plots, and lands. By creating an integrated model in which the shoreline change numerical model and hedonic value numerical model work together, a value estimation application was made with the integrated model for a selected coastal real estate property. Findings indicate that a property valued at TL 155,994.57 in 2021 is projected to depreciate to TL 35,878.75 b y 2028 due to anticipated submersion from shoreline changes. The study underscores the negative value implications of properties nearing the shoreline and the positive value implications of those distancing from it.

Volumetric Add-On Retrofit Strategy with Multi-Benefit Approach toward Nearly Zero Energy Buildings Target

Around 35% of the total housing stock of the European Union is more than half a century old. The shortage of funds for new construction, combined with rapidly changing economic, social, and technological factors, has led to significant obsolescence. Additionally, this situation makes it difficult to satisfy the owners’ energy, functional, and socio-economic needs. This research aims to develop an innovative retrofit approach that brings multiple benefits to assessing retrofit designs for social housing, with specific emphasis on volumetric envelope additions toward the nearly zero energy buildings target (nZEBs). To achieve the purpose of this study, the research through design methodology was chosen. The research methodology consisted of two phases: design and simulation. First, the design phase focused on re-designing and retrofitting social housing to address various aspects of the functional requirements in developing rational solutions. Second, the simulation phase focused on computational modeling and analysis of energy performance to assess the nZEBs target. The results show that the use of high-efficiency Heating, Ventilation, and Air Conditioning (HVAC) systems and improved material envelopes cut electricity consumption use by 43% and primary energy use by 40% compared to the base case. Photovoltaics (PV) production can meet the total electricity demand for six months. This approach can encourage residents and tenants to actively participate in the retrofit process and increase the real estate value of buildings through improvements in energy efficiency and housing function.

Enhancing Property Valuation in Post-War Recovery: Integrating War-Related Attributes into Real Estate Valuation Practices

In post-war environments, property valuation encounters obstacles stemming from widespread destruction, population displacement, and complex legal frameworks. This study addresses post-war property valuation by integrating war-related considerations into the ISO 19152 Land Administration Domain Model, resulting in a valuation information model for Syria’s post-war landscape, serving as a reference for property valuation in conflict-affected areas. Additionally, property valuation is enhanced through visualization modeling, aiding the comprehension of war-related attributes amidst and following conflict. We utilize data from a field survey of 243 Condominium Units in the Harasta district, Rural Damascus Governorate. These data were collected through quantitative interviews with real estate companies and residents to uncover facts about property prices and war-related conditions. Our quantitative data are analyzed using inferential statistics of mean housing prices to assess the impact of war-related variables on property values during both wartime and post-war periods. The analysis reveals significant fluctuations in prices during wartime, with severely damaged properties experiencing notable declines (about −75%), followed by moderately damaged properties (about −60%). In the post-war phase, rehabilitated properties demonstrate price improvements (1.8% to 22.5%), while others continue to depreciate (−55% to −65%). These insights inform post-war property valuation standards, facilitating sustainable investment during the post-war recovery phase.

The Impact of Rail Transport Infrastructure on Residential Property Rental Values in Kubwa, Abuja, Nigeria

On a global scale, numerous research studies have been embarked upon to show the level of relationship between transport facilities and residential rental values. This study dwelt on the various aspects of rail infrastructure and how it impacts on residential rental values. To accomplish the aim, questionnaires were distributed to head of households and practicing Estate Surveyors and Valuers in Kubwa, Nigeria. Physical observation also aided the researcher in data gathering. Data were interpreted using tables and percentage while Karl Pearson product moment correlation with the aid of Statistical Package for Social Sciences was employed for data analysis. From the correlation analysis the result revealed that the value of r (Pearson correlation) is 0.751 which indicates strong relationship. The p-value (.001) was less than 0.05 significant level. It was confirmed that the rail transport infrastructure has significant impact on residential rental values in Kubwa, Abuja. This translates to growth in rental values. The study recommended government initiatives on the rail system which directly increases the quantity of optimum rail lines of high-frequency metro lines resulting to a substantial increase in real estate values. This study has provided firsthand information and guide for property investors, estate surveyors, built environment professionals as well as government agencies on the importance of rail infrastructure and the various ways it can positively or negatively affect rental values.

Predictive Assessment of the Interior Quality of Apartments Based on Multimodal Data with Variable Visual Input

Multimodal analyses in the context of automated real estate valuation (AVM) offer the possibility of enriching the models with additional information, which benefits the accuracy of the models. However, this variety of data can overwhelm common machine learning models, which generally only process certain data modalities and only a fixed data quantity. This leads to a bottleneck in information processing, as in many cases much more information is available per observation, of which only a single selection or sample is used to train the algorithm and the remaining information is disregarded. We propose a multimodal network architecture that incorporates both textual and visual inputs and fuses their information. Furthermore, we introduce a training strategy that can take advantage of a variable number of input images for each real estate object. In our experiments, we test and compare several unimodal (baseline) models with our multimodal architecture. Our approach shows several advantages in terms of model performance over unimodal approaches. The results show the best performance for the multimodal model with a variable number of visual inputs, as well as improved prediction for the underrepresented classes of indoor quality, mitigating the effects of unbalanced data. With the presented approach, which efficiently combines and merges multiple data modalities, we have shown how such a method can be easily adapted to an AVM for the extraction of supplementary information.

Соціотопографія Вільнюса в XVII–XVIII ст.: основні особливості та напрямки розвитку

The article summarises the characteristics of the development of the Vilnius community in the Grand Duchy of Lithuania, regarding the context of historical events between the 17th and 18th centuries. The summary is part of a scientific project carried out between 2018 and 2021, based on a research of real estate registers and lists of taxes collected by the magistrate. The research used a statistical method, a spatial relational database and the structured query language (SQL) as well as Python programming language libraries NumPy and Pandas for managing and analysing big data. A detailed description of the study and its results can be found in the collective monograph published in 2021. Based on these sources, the value of real estate in different districts of Vilnius, spatial distribution, size and function of the buildings as well as the distribution of their owners in the city between first half of the 17th and the end of the 18th century were analysed. It was determined that frequent large fires were causing the biggest changes to the spatial structure of Vilnius. The greatest changes in demography and sociotopography were recorded after the occupation of Vilnius by the Moscow army between 1655 and 1661 and plague outbreaks in 1657 and between 1709 and 1710. During these times, the city has lost between 30 and 50 percent of its population. As a result, the mediaeval spatial structure of the old city gradually disappeared and its current appearance was formed with large, almost completely built-up plots of land, elite residences and dominant brick architecture of the Baroque style. The social structure has been partially changed. Poor townspeople and some artisans moved from the city centre to the suburbs. In the fortified centre, most real estate passed from the hands of ordinary citizens to wealthy individuals and organisations, who often acquired entire groups of empty land areas. However, some characteristics of the sociotopography of the 16th and first half of the 17th centuries remained unchanged, including the locations of some areas where social groups were living.

Comparative Performance Analysis of Türkiye's Land Management System Using SWOT Analysis Method

With sustainability and digitalization, countries are urgently conducting technical studies as well as legal and economicstudies in land management. In this study, land management models developed by 12 countries with different landmanagement systems were analyzed according to the qualitative research method. The data obtained by the selectedcountries have been examined under these headings: land management, registered data, legal side, technicalinstitutions, digitalization studies and technical models. Comparison tables of the countries were created under thedetermined headings. In addition, a comparison table was created by examining the land management data modelsdeveloped by the countries. The performance analysis of the land management studies in Türkiye (In terms of Planning,Cadastre, Land Infrastructure, Real Estate Valuation) has been made by using the SWOT Analysis method of evaluationof these countries. The strengths and weaknesses of Türkiye's land management system were evaluated as a result ofthe analysis.

국제회계기준(IFRS) 도입에 따른 부동산 가격이 국가 재정건전성에 미치는 영향

Due to the COVID-19 virus outbreak, inevitable welfare spending has increased sharply, and as the national debt has increased sharply, international credit rating agencies have considered downgrading Korea's credit rating. However, the Korean government has provided support for supplementary budgets and welfare policies, and as a result, national debt is increasing and national Financial soundness is deteriorating. Accordingly, based on previous studies, this study aims to present real estate-related policy measures that can fundamentally improve the national fiscal soundness so that the national credit rating is not degraded by studying how asset revaluation gains and comprehensive real estate taxes caused by real estate price increases affect national fiscal soundness indicators based on International Accounting Standards (IFRS). As a result of the study's analysis, the introduction of international accounting standards resulted in conditional results that 'an increase in the declared value of real estate can be positive for the national fiscal soundness', and in this study, four policy improvement measures related to real estate were proposed. Through these policy implications and fiscal policies such as institutional improvement measures, the government is expected to improve the national fiscal soundness while maintaining the national credit rating.

Does public rental housing foster social ties? A study of the everyday social lives of rural migrants in Chongqing, China

ABSTRACT Public housing is a crucial means of providing sufficient housing solutions worldwide. However, many experts criticize the harsh architecture, the absence of public space, and lacking social infrastructure in these densely populated public housing projects in the Global North. In China, previous research has mainly focused on the effect of public rental housing (PRH) on real estate values, employment trajectories of rural migrants, and their life challenges. Few studies have explored the dynamic relationship between rural migrants, their daily social life, and how it interacts with the environment. By conducting multiple site observations and 120 semi-structured interviews with rural migrants living in three different PRH sites in Chongqing, this study addresses these gaps and uncovers the complexities of their daily social lives. The findings show that rural migrant residents have established neighborhood networks, provided mutual support, and developed a sense of place attachment to their new homes through various strategies in their daily social lives. The research enriches the theoretical debate on migration theories and deepens our understanding of rural migrants’ social experiences in urban settings. It also sheds light on China’s sustainable urban development and the overall well-being of migrating populations.

Beyond displacement: The role of real-estate valuations in shaping urban displaceability

This paper brings the conceptual and methodological apparatus of the relational approach to the emergent conversation on displaceability – which is a novel concept underscoring the perpetual risk in urban environments of potential eviction, highlighting the ever-present tension between belonging and upheaval in contemporary urban settings. Drawing from case studies in Tel-Aviv and occupied East Jerusalem, I delve into two distinct local real estate valuation mechanisms: Tel Aviv’s Standard-21, which assesses project feasibility, and Jerusalem’s Article-219, a directive for appraising unauthorized construction. Set against Tel Aviv’s market driven context and the Israeli occupation of East Jerusalem, these mechanisms elucidate the local nuances of displaceability. Together, these cases underscore how valuation techniques perpetuate displaceability as a consistent and normalized facet of urban development. Yet, the paper also demonstrates how displaceability engenders resistance, as the acceptance of displacement as an inevitable urban reality prompts potential displacement victims to utilize the same socio-technical tools pointed against them to challenge this reality. Beyond a mere global overview, this paper delves into how displaceability is shaped by specific local socio-technical conditions. By emphasizing these nuances, it unravels both the intricate influences of regional factors and the overarching global implications of displaceability.

Addressing Methodological Challenges in the Mass Real Estate Valuation Process, Specifically Within the Context of the Republic of Moldova

This research investigates the precision of mass real estate valuation models, particularly focusing on the methodology of mathematical value modeling. The discussed challenges are particularly relevant to nations employing an ad-valorem taxation system. The Republic of Moldova's mass appraisal system for real estate serves as an illustrative case. Through a comparative analysis of the performance metrics between existing models and those refined based on the proposed recommendations, the study illustrates the improved efficiency of the advocated methodology.

RETROSPECTIVE OF THE UKRAINIAN REAL ESTATE MARKET. CHANGES AND TRENDS

The article aims to study the secondary segment of the real estate market of Ukraine using geographic information systems. The authors research the secondary real estate market for four control time points of 2021, 2022, 2023, and 2024 in all regions of Ukraine. The article conducts a retrospective analysis of real estate values and rental rates based on data from open sources of information. Using the authors’ methods of statistical data collection, we analyse 10,604,201 real estate objects. It is noteworthy that property prices in settlements located in the frontline zone (areas of hostilities) are declining. In addition, there is a tendency to increase the number of offers for the sale of real estate, but the demand for the purchase has significantly decreased due to the high risk of destruction. At the same time, the demand for rental property has increased substantially, likely due to the large number of displaced persons leaving the regions and military personnel arriving instead. The article calculates the capitalisation rate and payback period on the Ukrainian secondary real estate market and makes conclusions about the trends in their changes from 2021 to January 2024. The study covers such segments of the secondary housing market as one-, two-, and three-room apartments. These calculations allow the authors to build informational and digital maps for all regions of Ukraine. The created infographic will assist investors in making well-informed decisions regarding the prospects for real estate market development in the future. It visualises the received data, allowing the analysis of large data sets in a shorter time interval. The article’s relevance lies in applying geographic information systems in studying a large volume of statistical information and creating information-digital maps by regions of Ukraine. The research result is information-digital maps with diagrams of real estate value changes in retrospect by regions of Ukraine. The results obtained by the authors will be relevant to realtors and appraisers for real estate value calculations and forecasting the situation of the real estate market in the future. Stakeholders can use conclusions made by the authors to determine the most attractive object for investment. Keywords: information and digital map, real estate, stakeholder, capitalisation rate, payback period, geographic information system.