Real estate appraisal is important for a variety of endeavors such as real estate deals, investment analysis, and real property taxation. Recently, deep learning has shown great promise for real estate appraisal by harnessing substantial online transaction data from web platforms. Nonetheless, deep learning is data-hungry, and thus it may not be trivially applicable to enormous small cities with limited data. To this end, we propose Meta-Transfer Learning Empowered Temporal Graph Networks (MetaTransfer) to transfer valuable knowledge from multiple data-rich metropolises to the data-scarce city to improve valuation performance. Specifically, by modeling the ever-growing real estate transactions with associated residential communities as a temporal event heterogeneous graph, we first design an Event-Triggered Temporal Graph Network to model the irregular spatiotemporal correlations between evolving real estate transactions. Besides, we formulate the city-wide real estate appraisal as a multi-task dynamic graph link label prediction problem, where the valuation of each community in a city is regarded as an individual task. A Hypernetwork-Based Multi-Task Learning module is proposed to simultaneously facilitate intra-city knowledge sharing between multiple communities and task-specific parameters generation to accommodate the community-wise real estate price distribution. Furthermore, we propose a Tri-Level Optimization Based Meta- Learning framework to adaptively re-weight training transaction instances from multiple source cities to mitigate negative transfer, and thus improve the cross-city knowledge transfer effectiveness. Finally, extensive experiments based on five real-world datasets demonstrate the significant superiority of MetaTransfer compared with eleven baseline algorithms.

Real estate sales contracts contain crucial information for property transactions, but manual extraction of data can be time-consuming and error-prone. This paper explores the application of large language models, specifically transformer-based architectures, for automated information extraction from real estate contracts. We discuss challenges, techniques, and future directions in leveraging these models to improve efficiency and accuracy in real estate contract analysis.

Real estate appraisal is a crucial issue for urban applications, which aims to value the properties on the market. Traditional methods perform appraisal based on the domain knowledge, but suffer from the efforts of hand-crafted design. Recently, several methods have been developed to automatize the valuation process by taking the property trading transaction into account when estimating the property value. However, existing methods only consider the real estate itself, ignoring the relation between the properties. Moreover, naively aggregating the information of neighbors fails to model the relationships between the transactions. To tackle these limitations, we propose a novel Neighbor Relation Graph Learning Framework (ReGram) by incorporating the relation between target transaction and surrounding neighbors with the attention mechanism. To model the influence between communities, we integrate the environmental information and the past price of each transaction from other communities. Moreover, since the target transactions in different regions share some similarities and differences of characteristics, we introduce a dynamic adapter to model the different distributions of the target transactions based on the input-related kernel weights. Extensive experiments on the real-world dataset with various scenarios demonstrate that ReGram robustly outperforms the state-of-the-art methods. Furthermore, comprehensive ablation studies were conducted to examine the effectiveness of each component in ReGram.

Brick-and-mortar real estate may seem like the only tangible thing left in an increasingly virtual world, but it too is being taken over by artificial intelligence. Some of the biggest names in the business, such as Compass, Zillow and LoanSnap, are now employing AI to help find buyers the perfect mortgage and the perfect home. And for real estate agents, it may already be a game-changer. Most real estate data is public, from land records to title documents, purchase price and even mortgage liens. The trouble was it was an onerous process to go to local offices and obtain all the information.

Fincase creates unique IT solutions in the real estate industry that allow you to fully optimize and automate the entire cycle of working with property. The company is using artificial intelligence and machine learning algorithms to transform the real estate appraisal market. The uniqueness of the company is in the creation of an approach to finding special solution for each partner. We do not come with a finished product, but create it together – applying the accumulated experience, and jointly identifying and solving problems. Fincase has been operating in PropTech sector since 2016.

Artificial intelligence in real estate is not just a buzzword today. Today AI has become an integral part of technology. AI has proliferated in many industry divisions, and real estate is no exemption. Opposed to what many people worried, the rise of AI hasn't led to a surge of jobs being displaced by computers, and neither is it likely to in the foreseeable future. Instead, AI arose from the need to obtain higher value from frequently large data sets, which have long since outgrown human intelligence to make sense of them.

While it was once considered an advanced technology of the future, artificial intelligence is very much a present-day reality. Thanks to inventions like self-driving cars, home assistant devices, automatic vacuum cleaners and remote home security solutions, Artificial Intelligence is on everyone's lips. Since AI seems to affect both the public and private sector, we started thinking about all the different ways in which it'll shake up the real estate world. Read on to find out more about the current and future impact of AI in property sales, marketing and operations. Artificial Intelligence, or AI for short, refers to smart technological tools whose level of awareness allows them to learn from their environment in order to improve processes and decision-making.

Despite unpredictable economic and political headwinds, the European real estate sector continues to flourish, albeit in some regions more than others. Competition for deals is fierce and speed is often of the essence: so much so that, according to research1 recently conducted by Drooms, over 50 per cent of real estate professionals in Europe are compromising on the quality of their due diligence in their rush to complete deals quickly. However, modern technology has a solution for those seeking to complete real estate transactions more efficiently. Our research also revealed that where time pressures on due diligence have led to a potential decrease in quality of the process, parties to a transaction have found a solution in technology enabled with artificial intelligence (AI), such as virtual data rooms. According to a Real Capital Analytics (RCA) report published in February 2018, Europe's commercial property investment market returned to growth in 2017, when it registered the third strongest annual expansion on record.2

The European real estate sector continues to flourish in regions such as the UK and Germany, despite strong and unpredictable economic and political headwinds. Successful transactions depend on high quality and detailed due diligence, but competition for the most lucrative deals can sometimes lead organisations to compromise on this stage of the process. The biggest challenge is that the size of real estate transactions is increasing exponentially because of regulatory and compliance requirements and also because of the broader volumes and types of documents involved. This means that more manual processes are required simply to find the right data. The trend for higher volumes and larger transactions in real estate, including higher levels of risk and multiple languages, has important implications for the way in which investment professionals manage the greater complexity of due diligence.