scholarly journals Verification Plan Using Neural Algorithm Blockchain Smart Contract for Secure P2P Real Estate Transactions

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1052
Author(s):  
Jun-Ho Huh ◽  
Seong-Kyu Kim
Keyword(s):  
Artificial Intelligence ◽  
Real Estate ◽  
Virtual Machine ◽  
Unified Modeling Language ◽  
Encryption Algorithm ◽  
The Real ◽  
The Real Estate ◽  
Elastic Curve ◽  
Smart Contract ◽  
Test Verification

Blockchain and artificial intelligence are the most important keywords in the Fourth Industrial Revolution. This study sought to apply these core technologies to future validated algorithms that make real estate transactions secure to come up with an encryption algorithm. In addition, the real estate transaction is being paid a large fee by the middlemen, the real estate agent. Furthermore and recently, P2P (peer-to-peer) real estate exchange is used a lot. However, these P2P real estate exchanges also have problems that have not been identified by each other between landlords and tenants. In particular, a research model was established to compare and verify the PBFT (practical Byzantine fault tolerance) algorithm of Hyperledger through the blockchain agreement process. Subsequently, a process for verifying the real estate contract was established. Through VM (virtual machine) research methodology for the verification of blockchain real estate contracts, ElGamal communication was provided to prove quantum cryptography. We also automated lightweight encryption test verification tools and blockchain smart contract VM (virtual machine) models using artificial intelligence. Verification was performed through a reservation server and a monitoring server using a test verification tool for network-based lightweight security IoT (Internet of things) GW (gateway). It presents important ECP (elastic curve program) and elastic curve Qu-Vanstone (ECQV) models among the main functions of the blockchain smart contract, and it is equipped with quantum-based encryption algorithm. In addition, the necessary UML (unified modeling language) source code and performance data were calculated according to the actual experimental environment, and the average value for blockchain for administrative or government authorized assets—4000 TPS (transaction per second) were tested. In the future, we want to use this technology for real estate transactions.

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 Blockchain and real estate: Dopo di Noi project

2020 ◽  
Vol 38 (2) ◽  
pp. 273-295 ◽  
Author(s):  
Marzia Morena ◽  
Tommaso Truppi ◽  
Angela Silvia Pavesi ◽  
Genny Cia ◽  
Jacopo Giannelli ◽  
...  
Keyword(s):  
Real Estate ◽  
Severe Disabilities ◽  
Trust Model ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
Blockchain Technology ◽  
Smart Contract ◽  
Real Estate Sector ◽  
Potential Applications

PurposeThis paper aims at investigating the possibility of effectively implementing the blockchain technology in the real estate environment, specifically applied to the Trust legal instrument in Dopo di Noi (After Us) project, which is intended to guarantee assistance to persons with severe disabilities.Design/methodology/approachThe paper is focused on how to apply the blockchain to the tool of Trust, analyzing the main features and characteristics of this technology.FindingsThe paper proposes two potential solutions for managing the Trust tool in the real estate sector, specifically within the Dopo di Noi project. The first simpler proposal is based on timestamping application. The second one radically changes the classical Trust model and introduces an automatization level in the process.Social implicationsThe paper presents potential applications of the blockchain technology within the framework of Dopo di Noi project, which allows among other features, legal and tax facilitation for the institution of Trusts to benefit persons with severe disabilities.Originality/valueThis paper highlights the potentiality of the combination of the blockchain technology and the real estate environment and applies the blockchain technology to the Dopo di Noi project. Specifically, with the second solution, the paper proposes a platform that gathers, in a single network, various elements of the blockchain technology, such as timestamping, smart property, smart contract, and links them in order to provide services to persons with severe disabilities.

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.

scholarly journals Improvements in the civil engineering focused on the real estate appraisal with the use of artificial intelligence and fuzzy logic

2020 ◽  
Vol 12 ◽  
pp. 44-52
Author(s):  
Vladimir Surgelas ◽  
Vivita Pukite ◽  
Irina Arhipova
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Real Estate ◽  
Civil Engineering ◽  
Central Area ◽  
Real Estate Market ◽  
The Real ◽  
The Real Estate ◽  
Property Evaluation ◽  
Real Estate Valuation

In the field of civil engineering, there are some traditional methods of property evaluation that deal with these techniques. However, there is controversy about what would bring the best performance, a greater degree of ease and clarity without presenting multicollinearity. This controversy is due to the difficulty of finding appropriate predictive variables in real estate valuation since they often do not fit the binary model, involving human subjectivity. From this, the research aims to propose improvements in the property evaluation process with the use of artificial intelligence without presenting the effects of multicollinearity and autocorrelation, to predict the value of the real estate market. The object of study is a standard 2-bedroom residential apartment with 48m-2 located in the central area of Jelgava, Latvia, in October 2019. Therefore, the methodology uses statistical inference as an initial analysis parameter and the fuzzy logic incorporates the best association rules which are originated from artificial intelligence extracted from the apriori algorithm. Finally, the results obtained by regression and fuzzy were compared with the value in euros m-2, according to the official publication of the government of Latvia, referring to the market value of a 2-bedroom residential apartment in the city of Jelgava, Latvia, in October 2019, this government publication is the reference for this study. The statistical hypotheses that allowed its validation were accepted. In the Fuzzy model, the results indicated an excellent equivalence to market prices in relation to the traditional valuation process.

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