scholarly journals Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence

2020 ◽  
Vol 12 (7) ◽  
pp. 2899
Author(s):  
Jun Kang ◽  
Hyun Jun Lee ◽  
Seung Hwan Jeong ◽  
Hee Soo Lee ◽  
Kyong Joo Oh
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Real Estate ◽  
Predictive Accuracy ◽  
Financial Economic ◽  
Forecasting Accuracy ◽  
The Real Estate ◽  
Auction Market ◽  
Forecasting Models ◽  
Auction Prices

The real estate auction market has become increasingly important in the financial, economic and investment fields, but few artificial intelligence-based studies have attempted to forecast the auction prices of real estate. The purpose of this study is to develop forecasting models of real estate auction prices using artificial intelligence and statistical methodologies. The forecasting models are developed through a regression model, an artificial neural network and a genetic algorithm. For empirical analysis, we use Seoul apartment auction data from 2013 to 2017 to predict the auction prices and compare the forecasting accuracy of the models. The genetic algorithm model has the best performance, and effective regional segmentation based on the auction appraisal price improves the predictive accuracy.

scholarly journals Civil Engineering Inspection for Real Estate Evaluation with the Use of Artificial Learning Algorithms and Fuzzy Logic

2020 ◽  
Vol 43 (338) ◽  
pp. 75-82
Author(s):  
Vladimir Surgelas ◽  
Irina Arhipova ◽  
Vivita Pukite
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Real Estate ◽  
Civil Engineering ◽  
Market Value ◽  
The State ◽  
The Real ◽  
The Real Estate ◽  
Technical Inspection ◽  
Physical Conditions

AbstractThe technical inspection of a building carried out by an expert in civil engineering can identify and classify the physical conditions of the real estate; this generates relevant information for the protection and safety of users. Given the real conditions of the property, and for the real estate valuation universe, using artificial intelligence and fuzzy logic, it is possible to obtain the market price associated with the physical conditions of the building. The objective of this experiment is to develop a property evaluation model using a civil engineering inspection form associated with artificial intelligence, and fuzzy logic, and also compare with market value to verify the applicability of this inspection form. Therefore, the methodology used is based on technical inspection of civil engineering regarding the state of conservation of properties according to the model used in Portugal and adapted to the reality of Latvia. Artificial intelligence is applied after obtaining data from that report. From this, association rules are obtained, which are used in the diffuse logic to obtain the price of the apartment per square meter, and for comparison with the market value. For this purpose, 48 samples of residential apartments located in the city of Jelgava in Latvia are used, with an inspection carried out from October to December 2019. The main result is the 9% error metric, which demonstrates the possibility of applying the method proposed in this experiment. Thus, for each apartment sample consulted, it resulted in the state of conservation and a market value associated.

scholarly journals Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1421
Author(s):  
Gergo Pinter ◽  
Amir Mosavi ◽  
Imre Felde
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Estate ◽  
Model Performance ◽  
Machine Learning Method ◽  
Learning Method ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Call Detail Records

Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel machine learning method is proposed to tackle real estate modeling complexity. Call detail records (CDR) provides excellent opportunities for in-depth investigation of the mobility characterization. This study explores the CDR potential for predicting the real estate price with the aid of artificial intelligence (AI). Several essential mobility entropy factors, including dweller entropy, dweller gyration, workers’ entropy, worker gyration, dwellers’ work distance, and workers’ home distance, are used as input variables. The prediction model is developed using the machine learning method of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle swarm optimization (PSO). Model performance is evaluated using mean square error (MSE), sustainability index (SI), and Willmott’s index (WI). The proposed model showed promising results revealing that the workers’ entropy and the dwellers’ work distances directly influence the real estate price. However, the dweller gyration, dweller entropy, workers’ gyration, and the workers’ home had a minimum effect on the price. Furthermore, it is shown that the flow of activities and entropy of mobility are often associated with the regions with lower real estate prices.

Artificial intelligence and real estate - not just an evolution, a real game changer!

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marcelo Cajias
Keyword(s):  
Artificial Intelligence ◽  
Real Estate ◽  
New Technologies ◽  
Current Status ◽  
Real Estate Industry ◽  
Expiry Date ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
Analyse Data

PurposeDigitalisation and AI are the most intensively discussed topics in the real estate industry. The subject aims at increasing the efficiency of existing processes and the institutional side of the industry is really interested. And in some ways, this is a breakthrough. This article elaborates on the current status quo and future path of the industry.Design/methodology/approachThe real estate industry is evolving, and parts of the business are increasingly being conquered by “proptechs” and “fintechs”. They have come into real estate to stay not because they discovered inefficiencies in the way one manages and does business with real estate, but because they come with an arsenal of new technologies that can change the whole game. The article discusses a path for changing the game in real estate.Findings“location, location, location” has now evolved to “data, data, data”. However, there is one essential aspect that must be considered before the latter can become the real value creator: the ability of market players to analyse data. And this does not mean being an excellent Excel user. The near future sees a solution called Explainable Artificial Intelligence (XAI) meaning that the econometric world constructed decades ago has an expiry date.Originality/valueOne needs to delete two myths from their mind: data quantity is proportional to accurate insights and that bringing your data to a cloud will deliver you with all the insights your business needs almost immediately.

scholarly journals Automation of business processes of entrepreneurial activity in the field of real estate based on artificial intelligence technologies

2021 ◽  
Vol 18 (5) ◽  
pp. 150-159
Author(s):  
A. N. Asaul ◽  
◽  
G. F. Shcherbina ◽  
M. A. Asaul ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Estate ◽  
Business Processes ◽  
Learning Technologies ◽  
Entrepreneurial Activity ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Sector ◽  
Entrepreneurial Activities

The article refines the concept of «business process», the essence of business processes` automation in entrepreneurial activities is considered through the use of artificial intelligence and machine learning technologies for IT integration in the real estate sector. Based on the market analysis, the state of development of artificial intelligence and machine learning in Russia, its significance and prospects for implementation in business activities in the real estate sector are studied.

scholarly journals Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review

2019 ◽  
Vol 11 (24) ◽  
pp. 7006 ◽  
Author(s):  
Daikun Wang ◽  
Victor Jing Li
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Real Estate ◽  
21St Century ◽  
Systematic Literature Review ◽  
Spatial Data ◽  
Property Tax ◽  
Future Trend ◽  
The Real Estate

With the increasing volume and active transaction of real estate properties, mass appraisal has been widely adopted in many countries for different purposes, including assessment of property tax. In this paper, 104 papers are selected for the systematic literature review of mass appraisal models and methods from 2000 to 2018. The review focuses on the application trend and classification of mass appraisal and highlights a 3I-trend, namely AI-Based model, GIS-Based model and MIX-Based model. The characteristics of different mass appraisal models are analyzed and compared. Finally, the future trend of mass appraisal based on model perspective is defined as “mass appraisal 2.0”: mass appraisal is the appraisal procedure of model establishment, analysis and test of group of properties as of a given date, combined with artificial intelligence, geo-information systems, and mixed methods, to better model the real estate value of non-spatial and spatial data.

GENETINIO ALGORITMO TAIKYMAS ŽEMĖS SKLYPŲ RIBŲ TOPOLOGIJAI OPTIMIZUOTI NEKILNOJAMOJO TURTO KADASTRO ŽEMĖLAPYJE / АЛГОРИТМ ДЛЯ ОПТИМИЗАЦИИ ТОПОЛОГИИ ГРАНИЦ ЗЕМЕЛЬНЫХ УЧАСТКОВ НА КАДАСТРОВОЙ КАРТЕ НЕДВИЖИМОГО ИМУЩЕСТВА

2011 ◽  
Vol 37 (2) ◽  
pp. 84-90 ◽  
Author(s):  
Irina Jonauskienė ◽  
Algimantas Zakarevičius ◽  
Vladislovas Česlovas Aksamitauskas ◽  
Dmitrij Šešok
Keyword(s):  
Genetic Algorithm ◽  
Experimental Study ◽  
Genetic Algorithms ◽  
Real Estate ◽  
Turning Points ◽  
The Real ◽  
The Real Estate

Discrepancies between adjacent parcel boundaries happen when marking the boundaries of land parcels in the real estate cadastre map, for example, gaps, overlaps etc. In cases where the coordinates of land turning points meet statutory admissibility, the topology of land boundaries is adjusted. The paper addresses issues related to the application of genetic algorithms for optimizing the topology of land boundaries. When applying the genetic algorithm, for the purpose of optimizing the topology of the boundaries of land parcels, methodological justification and experimental study are employed. Santrauka Žymint žemės sklypų ribas nekilnojamojo turto kadastro žemėlapyje pasitaiko nesutapimų tarp gretimų sklypų ribų, t. y. atsiranda tarpai arba susidaro sanklotos. Tais atvejais, kai žemės sklypų posūkio taškų koordinatės atitinka teisės aktais nustatytus leistinumus, derinama žemės sklypų ribų topologija. Straipsnyje nagrinėjami klausimai, susiję su genetinių algoritmų taikymu žemės sklypų ribų topologijai optimizuoti. Atliktas genetinio algoritmo taikymo tam tikslui metodologinis pagrindimas ir eksperimentinis tyrimas. Резюме При обозначении границ земельных участков на кадастровой карте недвижимого имущества между границами смежных участков могут возникнуть расхождения, то есть появиться пробелы или пересечения. Когда координаты границ земельных участков не превышают предусмотренной правовыми и нормативными актами приемлемости, корректируется топология границ земельных участков. В статье рассмотрены вопросы, касающиеся применения генетических алгоритмов для оптимизации топологии границ земельных участков. Разработана методология применения генетического алгоритма для оптимизации топологии границ земельных участков и выполнено экспериментальное исследование.

scholarly journals FEATURES OF PROJECT FINANCING OF REAL ESTATE IN THE DIGITAL ECONOMY AND ITS STATE REGULATION

2020 ◽  
pp. 152-159
Author(s):  
A. V. Zakharova ◽  
V. G. Makeeva ◽  
N. V. Kazantseva ◽  
O. A. Revzon
Keyword(s):  
Real Estate ◽  
Banking Sector ◽  
Financial Economic ◽  
Legal Aspects ◽  
Real Estate Market ◽  
State Regulation ◽  
Project Financing ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Real Estate Financing

The problematic issues related to investment processes in the real estate market, based on modern tools of project financing, have been considered. The Russian experience of taxation of investment entities in the Russian Federation, the experience of private-public partnership in the process of investing in real estate objects, have been studied, the problems of using project financing by real estate market entities in the conditions of digitalization have been identified. The directions of improvement of financial, economic, social and legal aspects of state regulation of project financing have been proposed in order to ensure a balance of interests of various economic entities, which as States, firms and households are considered. Modern models of real estate financing by the banking sector have been reviewed.

Digital Transformation of the Real Estate Segment With Big Data and Marketing Analytics

2021 ◽  
pp. 357-376
Author(s):  
Luciano de A. Barbosa ◽  
Sérgio Ricardo Goes Oliveira ◽  
Joao Rocha Jr. ◽  
Emanuele Marques ◽  
Sérgio Maravilhas
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Real Estate ◽  
Economic Importance ◽  
Digital Transformation ◽  
The Real ◽  
The Real Estate ◽  
Marketing Analytics ◽  
Start Up

In this chapter, the birth of a Brazilian start-up is analyzed against the background of the digital transformation of the real estate segment. First, the authors describe the economic importance of the sector and its operation. Then they present the platform that makes use of advanced techniques in the areas of artificial intelligence, visualization, management, and data processing. This platform helps to capture wealth and amount of data in real-time, demonstrating the revolutionary potential of the era of big data and consumer analytics. The text explores the changes that the platform imposes on the traditional real estate model while detailing how the decision-making process in this sector is impacted.

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