Research On Blockchain-based Housing Rental Transaction Mechanism

In recent years,an increasing number of migrant individuals have been relocating to economically developed and resource-rich cities,leading to a surge in the real estate sector.However,speculation and inflated prices by certain individuals have resulted in higher prices in cities,prompting financially strained young people to turn to renting.In the current housing rental industry,real estate agents or third-party platforms often utilize information barriers and bundle consumption to generate high service fees,which significantly impact the rental experience for both parties involved in the transaction.Additionally,phenomena such as fraudulent listings and breaches of contract in the leasing process have also been observed.The emergence of blockchain technology presents a potential solution to address these issues.To this end,we propose a blockchain-based housing rental transaction mechanism that leverages the unique features of decentralization,transparency,and immutability to enhance transaction efficiency and user experience in the rental process while establishing a secure and trustworthy transaction environment.The primary objectives of this paper are as follows:(1)Taking into account the process and characteristics of traditional housing leasing services,a transaction framework applicable to housing rental is proposed based on blockchain.To make the leasing process more convenient,combining smart contracts to automate the settlement of housing leasing transactions.Transactions are bundled and broadcasted across the network to enable a transparent and auditable transaction process between tenant and landlord without the need for centralized institutions.(2)To enhance the efficiency of the housing leasing process and improve the transaction experience of the leasing parties,a transaction matching optimization model for housing leasing users in a blockchain environment is constructed.The model presents a satisfaction evaluation method based on exact numbers,interval numbers,and linguistic phrases,taking into account the differentiated transaction needs of both tenant and landlord as well as the ambiguity and complexity of the index evaluation information of the rental entities.Through a comparison of expected and actual information,the final model aims to maximize the satisfaction of both parties,enabling the leasing parties who meet each other’s conditions to reach a swift agreement.(3)In order to address the issue of transaction matching optimization in a constrained timeframe,an algorithmic design for matching housing lease transactions is proposed.The teaching-learning-based optimization algorithm(TLBO)is utilized to achieve optimal matching of lease subjects with a fast running speed and minimal parameters.To improve the performance of the algorithm and prevent the search process from being trapped in a local optimum,a TLBO algorithm based on the normal cloud model(CTLBO)is proposed.To address the limitations of the teaching factor during the "teaching" phase,the teaching factor is optimised to an adaptive dynamic teaching factor,along with a learning factor to avoid premature population convergence.The "learning" phase of the algorithm is enriched by introducing inertia weights and combining them with the normal cloud model.The combination of these enhancements provides a balanced approach that allows the algorithm to achieve both global exploration and local exploitation.The feasibility and effectiveness of the blockchain-based housing lease transaction scheme are demonstrated through the analysis of algorithmic examples and simulation experiments.Compared with traditional leasing methods,the proposed scheme can effectively reduce the risk of the transaction for both parties involved and has lower execution costs.