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Journal articles on the topic 'Real Estate Data'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Kim, Jin. "Research on Real Estate Needs Using Big Data." Korea Real Estate Society 76 (June 30, 2025): 119–37. https://doi.org/10.37407/kres.2024.43.2.119.

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Recognizes that the rapidly changing, social structure and accelerating technological advancements— particularly digital transformation—are bringing significant changes to the, real estate market. Changes in consumer lifestyles and the diversification of values have had complex effects on the needs for residential and commercial, spaces. Accordingly, methods for analyzing real estate demand are becoming increasingly sophisticated. Against this backdrop, the study sought to systematically analyze the needs, of modern real estate consumers through big data and to quantitatively identify shifts i

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Schernthanner, Harald, Hartmut Asche, Julia Gonschorek, and Lasse Scheele. "Spatial Modeling and Geovisualization of Rental Prices for Real Estate portals." International Journal of Agricultural and Environmental Information Systems 8, no. 2 (2017): 78–91. http://dx.doi.org/10.4018/ijaeis.2017040106.

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From a geoinformation science perspective real estate portals apply non-spatial methods to analyse and visualise rental price data. Their approach shows considerable shortcomings. Portal operators neglect real estate agents' mantra that exactly three things are important in real estates: location, location and location (Stroisch, 2010). Although real estate portals retacord the spatial reference of their listed apartments, geocoded address data is used insufficiently for analyses and visualisation, and in many cases the data is just used to “pin” map the listings. To date geoinformation scienc

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Cajias, Marcelo, and Anett Wins. "Data intelligence and real estate – machines are the real game changer." Journal of Property Investment & Finance 40, no. 3 (2021): 306–10. http://dx.doi.org/10.1108/jpif-11-2021-0101.

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PurposeThe paper shows with two concrete examples about how algorithms are used in active real estate management. The paper also highlights that the discussion about the adoption of new technologies is crucial for market players.Design/methodology/approach The authors review the current status quo about new technologies in real estate and provide two examples of how algorithms can be used to understand locations and the value drivers of rents.FindingsLocation, location, location is nowadays data, data, data coupled with the knowledge of how to create life out of data. Algorithm can help to und

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KIRICHEK, Yu O. "CADASTRAL REGISTRATION OF REAL PROPERTY WITH DATA DIGITALIZATION." Ukrainian Journal of Civil Engineering and Architecture, no. 5 (December 27, 2021): 50–55. http://dx.doi.org/10.30838/j.bpsacea.2312.261021.50.800.

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Problem statement. The transition in Ukraine from registration and accounting of land in the State Land Cadastre to registration and accounting of real estate in the multi-purpose cadastre requires research aimed at optimizing the information of the multi-purpose cadastre and the form of data representation based on their digitalization to limit the size of the database by technological support. The composition and scope of real estate accounting information data determine the purpose and functions of the multi-purpose cadastre. The problem is the huge amount of information about real estate a

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Sinenko, V. "Classification of errors in details Single roster of real estate in verification of data." Bulletin of Science and Practice 4, no. 10 (2018): 384–90. https://doi.org/10.5281/zenodo.1462019.

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In connection with the entry in 2017 of the Federal Law of July 13, 2015 no. 218-FZ. On state registration of real estate, one of the priority and important tasks in the registration and registration system is the mechanism for correcting errors contained in the real estate cadastre. In practice, the presence of errors in the register of real estate remains the most urgent and global problem, requiring an immediate decision to identify and correct. The investigated problem is the inconsistency and duplication of the information of the real estate cadastre system and the Unified State Regi

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Ersoz, Filiz, Taner Ersoz, and Muhammet Soydan. "Research on Factors Affecting Real Estate Values by Data Mining." Baltic Journal of Real Estate Economics and Construction Management 6, no. 1 (2018): 220–39. http://dx.doi.org/10.2478/bjreecm-2018-0017.

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Abstract Construction sector has an important place in Turkey’s economy. Real estate sales for the sector are increasing in parallel. However, the purchase cost is also important for those who are willing to buy a real estate. In the acquisition of real estate, factors such as size, location and age of the house are taken into consideration. The aim of the article is to conduct research on factors affecting real estate values by data mining. In this study, the most important variables that determine the value of the real estate have been investigated by data mining methods. The research has be

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Chen, C. X., H. Zhang, K. Jiang, et al. "RESEARCH ON EVALUATION METHOD OF REAL ESTATE REGISTRATION DATA QUALITY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 29, 2021): 749–54. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-749-2021.

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Abstract. In recent years, China has promulgated the "Civil Code of the People's Republic of China", "Implementation Rules of the Provisional Regulations on Real Estate Registration" and other laws and regulations, which have protected citizens' rights and obligations in real estate from the legal system. It shows that the quality of real estate registration data is very important. At present, there is no set of standards for evaluating the quality of real estate registration data. This article sorts out the production process of real estate registration data and focuses on the four stages of

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Conover, C. Mitchell, Joseph D. Farizo, H. Swint Friday, and David S. North. "The Diversification Benefits of Foreign Real Estate: Evidence from 40 Years of Data." Journal of Risk and Financial Management 17, no. 4 (2024): 160. http://dx.doi.org/10.3390/jrfm17040160.

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We investigate the potential of global real estate to improve the long-term performance of a US equity portfolio, utilizing a recent dataset of 40 years’ worth of US stocks, US real estate, 13 foreign stock markets, and 13 foreign real estate markets across diverse regions. Despite a modest performance in terms of risk and return, foreign real estate has consistently lower correlations with US stocks compared to foreign equities. Rolling correlation analysis indicates that foreign real estate markets remain relatively segmented compared to foreign equity, despite increasing financial market co

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9

Ashok, Dasari. "Estimating the Price of Real Estate Properties with the Help of Online Images." ECS Transactions 107, no. 1 (2022): 16933–41. http://dx.doi.org/10.1149/10701.16933ecst.

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When it comes to property investment pricing, it may be tough for homebuyers to understand and compare. A number of estate evaluation methods have been developed in order to analyze real estate, however they are constrained by the traditional data sources for estate assessment. Potential exists, however, to collect and analyze property-related mobile data when new ways of doing things arise. Use estates' geographical dependency to increase the value of a property. The New York Times reports that a property's value may be determined by its own characteristics, the value of neighboring homes in

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10

Sun, Ziyang. "Research on Product Decision of Real Estate Project Based on Big Data Analysis." Frontiers in Business, Economics and Management 11, no. 2 (2023): 220–23. http://dx.doi.org/10.54097/fbem.v11i2.12593.

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The characteristics of product decision-making of real estate projects are determined by the particularity of real estate projects. Because the development and construction period of real estate projects is long, the investment is large, many parties participate, there are many risk factors in the development process, and after the development is completed, it is unrepeatable, and most of them aim at profit. Real estate regulation and control is a long-term work, and it is urgent to establish a set of operational analysis and decision support platform that runs through the whole chain of real

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11

Cui, Jian, Ji Ju Ma, Dong Ling Ma, and Na Na Yang. "Application of Three-Dimensional Visualization Technology in Real Estate Management." Applied Mechanics and Materials 580-583 (July 2014): 2765–68. http://dx.doi.org/10.4028/www.scientific.net/amm.580-583.2765.

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Real estates are the material basis of the survival and development of a city, and are important parts of urban development. We use 3ds Max as a tool to create 3D models of buildings and Supermap Deskpro.NET as a platform to make an effective combination of 3d models, real estate graphic data and real estate attribute data. Three-dimensional display of surveying and mapping information and three-dimensional visualization management of house property information are realized.

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SHIBASAKI, Ryosuke, and Chihiro SHIMIZU. "Data Resources in Real Estate Studies." Japanese Journal of Real Estate Sciences 33, no. 4 (2020): 55–57. http://dx.doi.org/10.5736/jares.33.4_55.

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13

Goel, Krupa. "Data-Driven Insights to Enhance and Optimize Sales Compensation Programs in Real Estate." American Journal of Engineering and Technology 07, no. 04 (2025): 75–96. https://doi.org/10.37547/tajet/volume07issue04-12.

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Sales compensation in the real estate sector is the most important factor in determining an agent’s performance and retention. Fixed salaries, straight commissions, and split commissions, along with other conventional compensation models, struggle to keep up with market changes, agent performance, and consumer preferences. Based on this, this paper studies how modern analytics techniques, such as predictive modeling and agent segmentation, can improve and optimize real estate sales compensation programs. These techniques also provide brokerages with ways to customize compensation plans, reward

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Gryanyk, Volodymyr. "AUTOMATION OF DATA PROCESSING IN THE UNIFIED SYSTEM OF CADASTRAL REGISTRATION OF REAL ESTATE." ENGINEERING, ENERGY, TRANSPORT AIC, no. 3(106) (November 29, 2019): 124–29. http://dx.doi.org/10.37128/2520-6168-2019-3-16.

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On the basis of the analysis of world achievements, the state of the cadastre and real estate, recommendations on the further development of the cadastre system in Ukraine are given. For the first time, a unified real estate cadastre registration System was proposed, which records the establishment and termination of immovable property rights, their restrictions and encumbrances, and contains the data necessary to fulfill the cadastre tasks in accordance with the requirements of the current legislation. The scientific bases of cadastral real estate registration have been worked out, the purpos

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15

Царькова, Н. И., and О. С. Конова. "Using Big Data technologies for real estate valuation." Informacionno-technologicheskij vestnik, no. 4(26) (December 15, 2020): 110–18. http://dx.doi.org/10.21499/2409-1650-2020-26-4-110-118.

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Основная цель статьи – показать на практике возможности технологий Big Data и проанализировать ситуацию на рынке недвижимости. В качестве примера используются данные онлайн площадок по купле/продаже недвижимости, содержащие в себе порядка миллиона строк. Для исследования рынка была произведена качественная оценка сложившейся ситуации в России в 2020 году с учётом последних событий. Исходные данные были использованы для поверхностного анализа и предварительной оценки складывающейся стоимости недвижимости в городе по административным округам. Анализ проводился при помощи языка программирования P

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Yang, Jian Ping, and Li Jun Hou. "Data Processing in Real Estate Industry for Selection of Strategic Alliance Partner for Real Estate Enterprise." Applied Mechanics and Materials 685 (October 2014): 731–34. http://dx.doi.org/10.4028/www.scientific.net/amm.685.731.

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Strategic alliance of real estate enterprise is the strategic choice responds to the current market environment and pursue sustainable development in real estate industry.While,the scientific selection of partner is the start and the key to success for establishing strategic alliance.Combination empowerment method and cloud model is introduced to the selection of strategic alliance partner for real estate enterprise.With the help of corresponding technology and equipment,through a series of data processing for data collection,processing, transformation and transmission proves the method can be

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17

Dou, Yuxin. "Utilizing 31 Chinese province panel data models to investigate the factors influencing house prices." Theoretical and Natural Science 39, no. 1 (2024): 121–28. http://dx.doi.org/10.54254/2753-8818/39/20240584.

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In the realm of data analysis, this research employs regression models to delve into the complexities of housing market dynamics. The construction sector of real estate development companies, the amount invested in real estate development, and the gross regional product have all been found to be important determinants of home prices. Interestingly, the most significant factor is the investment in real estate development, which has a significant impact on house prices. The analysis reveals a positive correlation between the gross regional product and investment amount in real estate development

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18

Guo, Yifan. "Analysis of Big Data Application in Real Estate Investment." Highlights in Business, Economics and Management 19 (November 2, 2023): 219–24. http://dx.doi.org/10.54097/hbem.v19i.11880.

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As an important project of national economic growth, real estate plays an important role in education, scientific research and production industries. Despite the slower pace of urbanization, However, the development of real estate has started to slow down currently, and there have been problems such as market differentiation and changes in consumer demand. At the same time, many investors' investments are blindly aimed at adapting to the market, which can easily lead to losses. With the development of the information age, big data can process massive amounts of information and provide investor

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19

Kuethe, Todd. "The risk and return of farm real estate and the “bad data problem”." Agricultural Finance Review 76, no. 1 (2016): 140–50. http://dx.doi.org/10.1108/afr-11-2015-0049.

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Purpose – The purpose of this paper is to explore the consequences of appraisal smoothing in the estimation of the risks and returns of farm real estate. It examines the degree to which the risk and return characteristics of farm real estate are an artifact of the methods used to measure aggregate property values. Design/methodology/approach – A multifactor asset pricing model is estimated using farm real estate returns in a manner consistent with prior research, as well as using farm real estate returns calculated using two synthetic unsmoothing procedures developed in the real estate finance

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20

Likos, Mustafa, Mahir Nakip, and Aytaç Gökmen. "Real Estate Marketing and Factors Impacting Real Estate Purchasing." International Journal of Applied Management Sciences and Engineering 6, no. 2 (2019): 15–35. http://dx.doi.org/10.4018/ijamse.2019070102.

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The purpose of the study is to introduce factors impact on purchasing behavior in real estate marketing. The seven factors analyzed—location, structural factor, neighborhood, social factor, reference group, financial, advertising—have a direct effect on real estate purchasing behavior. A questionnaire prepared for this purpose was applied to 235 randomly selected people and the collected data were analyzed by variance analysis method. According to the results of the statistical analysis, factors such as location, structural factor, social factor, neighborhood, financial, advertising factor inf

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Mamedli, M. O., and A. V. Umnov. "Real estate valuation based on big data." Voprosy Ekonomiki, no. 12 (December 2, 2022): 118–36. http://dx.doi.org/10.32609/0042-8736-2022-12-118-136.

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The paper considers the application of the web scrapping and machine learning algorithms for the assessment of the real estate price on the secondary housing market in Moscow. For this, we collect and process the data from the CIAN website and the data from “Reforma GKH”. To evaluate real estate objects, we consider such machine learning algorithms as Elastic Net, Random Forest and Gradient Boosting. We also apply Shapley vector-based approach to interpret the results of the black-box algorithms. The results suggest that the use of black-box algorithms in assessing the price of apartments on t

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Manan patel, Ankit Kumar s. patel, Jayraj v Solanki, and Paresh Kumar H. Patel. "Impact of RERA on Real Estate." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 08 (2024): 2491–94. http://dx.doi.org/10.47392/irjaem.2024.0359.

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In 2016, the Real Estate (Regulation and Development) Act (RERA) was enacted in India with the aim of stimulating growth in the housing sector. RERA serves as the regulatory authority overseeing the real estate domain in the country. It mandates that developers, buyers, and sellers adhere to its prescribed rules and regulations. RERA holds legal authority to supervise the construction of estates, buildings, and flats, as well as to regulate minimum selling prices of apartment houses. To assess the impact of RERA on the real estate sector, a questionnaire was devised in collaboration with real

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Yang, Jian Ping, and Yan Zhang. "Data Processing in Risk Evaluation of China’s Old Real Estate." Applied Mechanics and Materials 685 (October 2014): 727–30. http://dx.doi.org/10.4028/www.scientific.net/amm.685.727.

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In the recent years, the old real estate has a broad development prospects in the real estate due to the aging of the population and the changes of pension. Because the old real estate project is not mature, so there are many potential risk factors in the process of development. This paper uses the risk matrix evaluation method to build old real estate development risk evaluation model. We take some specific data into the model, then can conclude that project risk assessment level through the calculation of the data.We can find out the key factors in the project development and judge the risk

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Sari, Yeptadian. "STUDI EMPIRIS PENERAPAN MANAJEMEN NILAI PADA PENGEMBANGAN REAL ESTATE DI SURABAYA." NALARs 16, no. 1 (2017): 85. http://dx.doi.org/10.24853/nalars.16.1.85-90.

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ABSTRAK. Penerapan sebuah metode dalam pengembangan real estat sangat dibutuhkan agar memastikan proyek berjalan dengan lancar. Terdapat metode yang mampu meningkatkan nilai proyek dan bahkan membuang biaya yang tidak perlu dalam proyek yang disebut dengan metode manajemen nilai atau value management (VM). Metode ini masih jarang digunakan di Indonesia namun sebagian besar pengembang real estat di Surabaya mengaplikasikannya. Penelitian ini mencari tahu tentang studi empiris dari penerapan VM pada pengembangan real estat di Surabaya, lebih spesifiknya yaitu mencari tahu tentang bagaimana VM di

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Akpaenyi, M. O., and P. Igwe C. "Analysis of the Viability of Commercial Real Estate Investment in Enugu, Enugu State, Nigeria (2012-2022)." International Journal of Real Estate 1, no. 1 (2025): 95–110. https://doi.org/10.5281/zenodo.14858667.

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Over the years, Enugu, the Capital City of Enugu State has witnessed a continuous rise in real estate investment especially in the residential aspect due to the nature of activities which was mainly administrative activities necessitated by the high presence of government offices in the State Capital and the consequent need for accommodation for government staff and officials. However, in recent times, there has been increase in business activities which has led to an increase in commercial real estate investment within the major towns in Enugu, Enugu State. This rise in commercial activities

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Chen, Shuangxin. "Digital Management Mode of Real Estate Marketing based on Big Data and Artificial Intelligence." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 12 (March 29, 2024): 269–80. http://dx.doi.org/10.37394/232018.2024.12.26.

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To cope with the pressure on sales information processing as the real estate industry grows, the study builds a real estate digital marketing management system design based on the analysis of real estate marketing needs to meet the needs of real estate marketers for digital information processing, and builds a hybrid recommendation model using a combination of Gradient Boosting Decision Tree (GBDT) technology and Logistic Regression (LR) to accurately recommend real estate potential purchase users. The GBDT-LR model performance test results show an accuracy of 94.63% and a regression rate of 9

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Pai, Ping-Feng, and Wen-Chang Wang. "Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices." Applied Sciences 10, no. 17 (2020): 5832. http://dx.doi.org/10.3390/app10175832.

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Real estate price prediction is crucial for the establishment of real estate policies and can help real estate owners and agents make informative decisions. The aim of this study is to employ actual transaction data and machine learning models to predict prices of real estate. The actual transaction data contain attributes and transaction prices of real estate that respectively serve as independent variables and dependent variables for machine learning models. The study employed four machine learning models-namely, least squares support vector regression (LSSVR), classification and regression

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TKACHYK, Olexandr. "APPLYING DATA CLUSTERING METHODS FOR CREATING TARGETING USER GROUPS FOR REAL ESTATE." Herald of Khmelnytskyi National University. Technical sciences 319, no. 2 (2023): 300–307. http://dx.doi.org/10.31891/2307-5732-2023-319-1-300-307.

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In this paper applied unsupervised clustering to a dataset examines the application of k-means clustering to create target user groups for a real estate platform. The goal is to segment the user base into meaningful groups to better understand their preferences and behaviors, and tailor marketing campaigns and product features to the needs of each group. The key step in the application of k-means clustering to real estate data is data preparation. Real estate data can be particularly messy and incomplete, and thus requires careful cleaning and normalization before clustering can be applied. Da

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Lv, Sheng. "Adaptive Grid Division and Cluster Analysis of Decision Graph in Real Estate Marketing Using Data Mining." Mobile Information Systems 2022 (April 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/4835540.

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China’s economy has been in a state of rapid development in recent years. People’s lifestyles and quality of life have also undergone earth-shaking changes. More people put forward requirements for the living environment and living environment, which promotes the rapid rise of the real estate industry. The state has promulgated several macrocontrol measures. Real estate companies analyze the needs of various customer groups, affordable housing unit prices, and family populations, and formulate real estate marketing strategies to improve real estate sales. Therefore, this paper practices data m

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Nkiruka, Evangeline Obi-Aso. "Performance of Foreign Direct Investment in Commercial Real Estate in Nigeria from 2006-2017." Journal of Economics, Finance and Management Studies 4, no. 07 (2021): 965–77. https://doi.org/10.47191/jefms/v4-i7-10.

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Performance of FDI in commercial real estate in Nigeria when compared to international benchmark figures is yet to be established and documented in literature. The aim of the study was to appraise the performance of FDI in commercial real estate in Nigeria from 2006 to 2017 in a bid to empirically assist foreign direct investors’ decisions on investing in the Nigerian commercial real estate sector. The objectives were to; ascertain and compare returns from foreign direct investments in commercial real estate in Nigeria with international benchmark; and to examine how FDI tax responsibili

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Grabovyy, Petr Grigor'evich, Roman Vyacheslavovich Volkov, and Valentin Valentinovich Volgin. "Intelligent real estate management." Real estate: economics, management, no. 1 (March 30, 2023): 16–20. http://dx.doi.org/10.22337/2073-8412-2023-1-16-20.

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The article analyzes the state and use of intelligent real estate management technologies. It is argued that intelligent real estate management is based on information and digital methods. The key factors in the transition to intellectual real estate management have been pointed out. Breakthrough technologies as a tool for the transition to intelligent management are considered. The analysis of digital assets, digital real estate, digital real estate passports is given. The importance of big data in real estate management is demonstrated. The influence of building information modelling (TIM) t

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Simons, Robert A., and Jesse Saginor. "International Real Estate Review." International Real Estate Review 13, no. 2 (2010): 134–56. http://dx.doi.org/10.53383/100123.

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This research evaluates the effect of leaking underground storage tanks (LUSTs) from gas stations on nearby commercial property when the existing data is incomplete or imperfect. While methodologies such as hedonic regression may be preferred for evaluating the effects of LUSTs on property values, the rigorous data requirements of these methodologies often cannot be met. Contingent valuation analysis is one method that enables estimation of losses when the data available is incomplete. A contingent valuation analysis of real estate professionals in South Carolina and Ohio provides estimates of

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Olaleye, Abel, and Benjamin Ekemode. "Integration between real estate equity and non-real estate equity." Journal of Property Investment & Finance 32, no. 3 (2014): 244–55. http://dx.doi.org/10.1108/jpif-10-2013-0063.

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Purpose – The paper examined the long-run relationship between real estate equity (property listed stock) and non-real estate equity (common stock) in the Nigerian capital market and established the integration between the investments. The paper aims to discuss these issues. Design/methodology/approach – The data collected comprised quarterly returns on property listed stock and All Share Index for the period of January 1999-December 2011. The calculated quarterly returns of the investments were subjected to the Philip-Person unit root test after which the integration between the investments w

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Al Abdulsalam, Ahmed Saif, Maged Mohammed Al-Baiti Al Hashemi, Mohammed Zayed Sulaiman Aleissaee, Abdelaziz Saleh Husain Almansoori, Gurdal Ertek, and Thouraya Gherissi Labben. "A Novel Data Analytics Methodology for Analyzing Real Estate Brokerage Markets with Case Study of Dubai." Buildings 14, no. 10 (2024): 3068. http://dx.doi.org/10.3390/buildings14103068.

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Despite the vast economic impact of real estate markets worldwide, research on real estate brokerage markets remains limited. Specifically, there are few studies that provide a systematic, integrated, and replicable analytical methodology to analyze and benchmark a given real estate brokerage market. To this end, this paper introduces a data analytics methodology for analyzing real estate brokerage markets, integrating various statistical and analytical methods to extract insights from market data, supporting real estate investment decisions. The applicability of the methodology is demonstrate

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Su, Liang, Li Lin, Shaozhen Chen, and Lin Li. "Effect Analysis of Real Estate Price and Monetary Policy: An Empirical Study Based on China Macroeconomic Data." Applied Economics and Finance 5, no. 2 (2018): 158. http://dx.doi.org/10.11114/aef.v5i2.2930.

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Since the reform of the housing system in 1998, China's real estate market has been gradually marketized. A large number of demand-side purchases that were suppressed by the planned economy were released. The rapidly rising real estate prices affected the People's Bank of China's monetary policy through Tobin's Q and the financial accelerator. This paper establishes a VAR model to analyze the policy factors and find out “land finance” has promoted the rise of real estate prices to a certain extent and the regulation of the real estate market lags behind; real estate prices can interfere with t

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Latifović, Faruk. "Public Presentation of Data on Real-Estate and Determination of Rights in Function of the Establishment of a Single Registry of Real-Estate and Real Rights to Real-Estate in the Brcko District of B&H." Geodetski glasnik, no. 45 (December 31, 2014): 46–62. http://dx.doi.org/10.58817/2233-1786.2014.48.45.46.

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The Law of Land Registry and Rights over Land of the Brčko District of B&H stipulated the establishment of a single registry of real-estate and real rights to real-estate in the Brčko District of B&H. Activities of the establishment of a single registry, in the segment of public presentation of data on real-estate and determination of rights, were entrusted to the Council for Presentation of Data on Real-estate and Establishment of Rights to Land of the Brčko District of B&H. In this paper, the author presents the inevitable short overview of the history of real-estate registries i

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Lv, Sheng. "Real Estate Marketing Adaptive Decision-Making Algorithm Based on Big Data Analysis." Security and Communication Networks 2022 (April 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/3443182.

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Aiming at the problems of low stability and efficiency of marketing decision-making and large complexity of marketing decision-making in the current marketing adaptive decision-making algorithm, a real estate marketing adaptive decision-making algorithm based on Big Data analysis is proposed. By analyzing the concept of Big Data, using the Big Data distributed computing architecture, researching the data mining-related algorithms. By constructing an association rule algorithm, mining the rules between real estate marketing and related factors. Based on the Spark-distributed computing platform,

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Kim, Jin. "A Study on the Forecasting of Real Estate Market Using Algorithms." Korea Real Estate Society 72 (June 30, 2024): 159–68. http://dx.doi.org/10.37407/kres.2024.42.2.159.

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Technology using algorithms has an impact on various aspects of the real estate market, such as real estate market price prediction, investment recommendation, and rent setting. First, it was confirmed that by improving the accuracy of real estate price prediction, real estate market price prediction using AI algorithm can derive more accurate results through machine learning and deep learning technologies based on past data. In addition, it is the development of an investment recommendation system. The investment recommendation system using AI can promote efficient fund management by discover

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Cui, Weijia. "The Impact of COVID-19 on the Real Estate Market Based on Case and Data Analysis." Advances in Economics, Management and Political Sciences 15, no. 1 (2023): 270–77. http://dx.doi.org/10.54254/2754-1169/15/20230928.

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In the previous two years, COVID-19 has been one of the pressing social issues, and empirical studies have shown that COVID-19 has a significant impact on the growth of the economy. However, more research and an analysis of the effects of COVID-19 on the global real estate market may be required. In order to better explore the effects of COVID-19 on the real estate market and the changes it has brought about, this study analyzes the real estate markets in China, the United States, and Europe together with pertinent real estate market statistics. According to the analysis, COVID-19 has caused a

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Shao, S. W., X. Huang, L. X. Xiao, and H. Liu. "EXPLORING THE IMPACT OF REAL ESTATE POLICY ON REAL ESTATE TRADING USING THE TIME SERIES ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 1281–87. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-1281-2020.

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Abstract. Housing price is a major issue affecting people's lives, but also closely related to the interests of the people themselves. Housing prices are affected by various factors, such as economic factors, population size factors, social factors, national policy factors, the internal factors of real estate and environmental factors. With the deepening of urbanization and the agglomeration of urban population in China, housing prices have been further accelerated. The Chinese government has also introduced a series of policies to limit real estate transactions and affect property prices. Thi

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Thrall, Grant, and Brandon Smith. "International Real Estate and Business Geographic Data." Journal of Real Estate Literature 12, no. 3 (2004): 375–94. http://dx.doi.org/10.1080/10835547.2004.12090148.

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DUBIN, ROBIN, KELLEY PACE, and THOMAS THIBODEAU. "Spatial Autoregression Techniques for Real Estate Data." Journal of Real Estate Literature 7, no. 1 (1999): 79–95. http://dx.doi.org/10.1080/10835547.1999.12090079.

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SHIMIZU, Chihiro. "Data Resources in Real Estate Market Analysis." Japanese Journal of Real Estate Sciences 33, no. 4 (2020): 58. http://dx.doi.org/10.5736/jares.33.4_58.

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Hromada, Eduard. "Real Estate Valuation Using Data Mining Software." Procedia Engineering 164 (2016): 284–91. http://dx.doi.org/10.1016/j.proeng.2016.11.621.

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Singh, Archana, Apoorva Sharma, and Gaurav Dubey. "Big data analytics predicting real estate prices." International Journal of System Assurance Engineering and Management 11, S2 (2020): 208–19. http://dx.doi.org/10.1007/s13198-020-00946-3.

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Ammerman, Sarah. "Leverage Real Estate Data During Prospecting Procedures." Major Gifts Report 26, no. 4 (2024): 8. http://dx.doi.org/10.1002/mgr.32348.

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Ritika, Shelkar, Sambare Vaishnavi, Vadali Ramkrishna, and Yerge Kaveri. "Data Analysis in Real Estate for Recommendation." Journal of Information Technology and Sciences 5, no. 1 (2019): 11–17. https://doi.org/10.5281/zenodo.2549203.

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Big Data has dependably been known as an unrest that will change the manner in which we live, work, and think. All can be comprehended by means of big information procedures can be utilized to handle and fathom numerous earnest issues in varying backgrounds, in the period of huge information. Traditional data analysis approaches are struggling when the data is in large amount. The revolution of Big Data can transform the way of living, working, and thinking that enables the process of optimization, insight discovery and decision making .The main idea behind this revolution is to make use of la

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Booth, P. M., and G. Marcato. "The Measurement and Modelling of Commercial Real Estate Performance." British Actuarial Journal 10, no. 1 (2004): 5–61. http://dx.doi.org/10.1017/s1357321700004530.

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ABSTRACTIn this paper we discuss methods of developing real estate indices, the availability of real estate data, the problems of using published real estate data and how real estate data can be used for stochastic investment modelling for actuarial purposes. In recent years there have been many developments in the collection, presentation and analysis of real estate data that have not found their way into the actuarial literature. We review those developments and suggest and develop ways in which raw real estate investment data can be used for actuarial purposes. We then review the Wilkie rea

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Dogan, Can, and John Can Topuz. "Real effects of real estate: evidence from unemployment rates." Studies in Economics and Finance 37, no. 4 (2020): 605–23. http://dx.doi.org/10.1108/sef-03-2019-0124.

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Purpose This paper aims to investigate the relationship between residential real estate prices and unemployment rates at the Metropolitan Statistical Area (MSA) level. Design/methodology/approach This paper uses a long time-series of MSA-level quarterly data from 1990 to 2018. It uses an instrumental variable approach to estimate the effects of residential real estate prices on unemployment rates using the geography-based land constraints measure of Saiz (2010) as the instrument. Findings The results show that changes in residential real estate prices do not have a causal effect on unemploymen

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Liu, Hua, and Nan Zhang. "Data Processing in the Key Factors Affecting China's Endowment Real Estate Enterprises Financing." Applied Mechanics and Materials 730 (January 2015): 349–52. http://dx.doi.org/10.4028/www.scientific.net/amm.730.349.

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Financing problem is one of the main reasons for restricting the development of endowment real estate enterprises in China. By analyzing the present situation of endowment real estate enterprises financing and researching relevant literatures, we sum up 20 general influence factors. Using the data processing model of the principal component analysis method to analyze the 20 general influence factors under the help of SPSS 19.0 statistical analysis software, we can find out the key influence factors which affect endowment real estate enterprises financing. Aiming at the key influence factors, w

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