This article is written by Anushka Merchant, pursuing a Certificate Course in Real Estate Laws from LawSikho.

Introduction

Artificial Intelligence (“AI”) refers to the use of complex computer software or programmes for the purpose of performing tasks similar to those performed by human intelligence. It makes use of smart technologies and algorithms in order to sense, adapt, evolve and respond to stimuli in the surrounding environment. These technologies are used in various fields for the purpose of increasing system automation, efficiency and performance.

Based on its functions, AI can be broadly divided into four kinds. Automated intelligence, which is used to automate manual, cognitive, routine and non-routine tasks. Autonomous intelligence, which is used to automate decision making processes without human intervention. Expanding intelligence, which is used to expand human self-expression; and supporting intelligence, which assists in the speedy accomplishment of certain tasks and targets. AI is most commonly used in agriculture, farming, manufacture, production, e-commerce; and for providing services like virtual assistance, autonomous flying, self-driving vehicles, security, surveillance, sports analysis, medical analysis, imaging analysis, livestock and inventory management and warehousing and supply-chain management.

The real estate sector has always been one to resist innovation and therefore, has been slow to integrate AI within its realm. Although there are many reasons for this, data availability, organization, standardisation, transparency and the legal aspects relating thereto act as the biggest obstacles to the onboarding of the technology. According to the Morgan Stanley Digitization Index of 2016, the real estate sector is the second least digitized industry. However, in recent years, governments, enterprises and other industry participants have recognized the potential of AI and are now focusing on data organization, digitization and transparency. This process will allow various algorithms to use predefined data and information to perform tasks and to make accurate and near-term predictions in order to give a boost to AI in various sectors.

Reshaping real estate

A deeper understanding of the potential applications of AI over the last decade has led to a new appreciation of its uses and advantages. The following are some of the ways in which AI is helping to reshape the real estate sector and its various aspects:

A. Automating Manual Tasks

Real estate transactions involve a great deal of documentation in the form of title deeds, land and revenue records, search reports, title certificates, litigation papers, zoning regulations, maps, plans, office orders etc. AI-powered tools can review these documents and scan them for completeness and for any inconsistencies and inaccuracies like missing signatures, invalid characters, empty fields etc. For instance, ‘iManage LLC’ has created a cloud network that assists professionals with work productivity management. It can assist real estate agencies by storing, analysing and managing contracts, lease terms, reports, etc. The in-built AI algorithm extracts important information from the documents to provide agencies with actionable insights.

B. Land Records, Mapping & Surveying

In most countries, land records consist of various documents that are maintained across different departments at the district and village levels. Often, these departments work in silos, the data across departments is not properly updated, surveys to update them are not undertaken or completed and inaccurate maps and plans are drawn up and used to establish the property boundaries on the ground. The poor maintenance of land records and discrepancies in property descriptions and boundaries create confusion, delays and disputes that can tie up land in litigation and adversely affect future transactions and title transfers. In India, research suggests that, on an average, land disputes take about twenty years to be resolved; and that land-related disputes account for two-thirds of all pending court cases in the country.

In order to improve the management and quality of land records and to increase access thereto, several countries have started using AI in the form of ‘Unmanned Aerial Systems’ (“UAS”) including drones, ‘High Resolution Remote Sensing’ systems, ‘Aerial and Satellite Imaging’ systems and ‘Remote Sensing and Earth Observation’ systems to survey lands, draw up maps and update land records, with minimum manpower involvement. These systems make use of devices and robots that are equipped with sensors, GPS systems and cameras that collect and monitor real-time and three-dimensional modeling data from the air, in cases where a high degree of precision is necessary.

Well maintained land records and orderly surveying and mapping of land has enabled some countries like the United Kingdom, Australia and Germany to adopt efficient land titling systems. In the United Kingdom and Australia, the government and in Germany, the courts, issue a title certificate to each landlord and landowner. This certificate assigns unassailable and indisputable ownership rights in immovable property to the registered titleholder and certifies their right, title and interest therein. In some cases, it is even accompanied with a title insurance. These systems do away with the need to conduct title due diligence, to draw up title reports and to record and register deeds prior to the transfer of property. 

In 2008, the Government of India, launched the ‘National Land Records Modernization Programme’ now known as the ‘Digital India Land Records Modernization Programme’ (“DILRMP”). DILRMP aims to modernize the management of land records, minimize property and land related disputes and increase transparency, with the ultimate goal of creating a system for guaranteeing conclusive and incontrovertible titles to immovable properties. This programme provides for the:

“computerization of all land records including mutations, digitization of maps and integration of textual and spatial data, survey/resurvey and updation of all survey and settlement records including creation of original cadastral records wherever necessary, computerization of registration and its integration with the land records maintenance system, development of core Geospatial Information System (GIS) and capacity building.”

It also envisages the employment of modernized equipment and methodology like AI, which guarantee a higher degree of precision and accuracy as compared to traditional methods of surveying, record preparation and management.

Thus, AI can prove to be beneficial for the purpose of land mapping and surveying and can help countries adopt a more foolproof system of maintaining and updating their records by eliminating human error and mischief. This can lay the foundation for land titling and title guarantee systems that they may want to adopt in the future.

C. Property Development Insights

AI technology can benefit developers and investors who are interested in developing a specific area. ‘Deepblocks’ and ‘CityBldr’ are AI platforms that use datasets to recommend plots and sites for construction and development based on availability, city zoning, construction capacity, industry trends etc. They come up with detailed insights on investment strategies and financial returns with adjustable assumptions.

D. Construction Management

AI can be used in under-construction projects to track the real-time interactions of workers and machinery on the construction site and potential safety issues, risks, errors and productivity issues. The following are some of the ways in which AI can provide for better construction management:

1. Predicting Realistic Time-lines: Data relating to historical time-lines including planned start and completion dates of previous or similar projects can be used by predictive models to envision realistic timelines for future projects. 
2. Risk & Hazard Management: Every construction project entails some risk in terms of quality, safety, time and cost. AI and machine learning solutions can be used for early detection of safety hazards and construction defects, which can be time and cost effective. They can also monitor and prioritize project risks at every stage of construction, which will enable the project team to focus its limited resources on the larger risk factors. 
3. Labour Allocation: Machine learning can be used to locate workers on the construction site, to evaluate work progress and to ascertain labour shortages. This assists project managers in locating sites that do not have enough workers and equipment to complete the project on schedule. They can deploy additional labour and machinery to these sites thereby providing for the better distribution of labour and machinery across the project. 
4. Preventing Cost Overruns: AI and artificial neural networks can be used to predict project cost overruns based on factors such as project size, cost of inventories, contract type, economic growth, market demand and the competence of workers and project managers. 
5. Design: AI and three-dimensional modelling can be used by architects and engineers to efficiently plan and design the buildings and infrastructure. ‘Generative Design’ is a form of machine learning that helps to identify and mitigate clashes between models generated by the different teams (architecture, engineering, mechanical, electrical and plumbing) during the planning phase and helps to explore all the possible alternatives and solutions. 
6. Progress Reporting: Robots can autonomously capture three-dimensional scans of the construction site and feed the data into a network to help classify the stages of construction of the project and sub-projects. A progress report will be updated at fixed intervals along with completion related statistics, which will enable the project team to regularly monitor and evaluate the progress. If time-lines aren’t met, the project team can step in to address the issues. ‘Doxel’ is a startup that uses AI to keep track of the installed items and automatically measures the earned value for each item.
7. Self-driving Machinery: Some repetitive tasks like pouring concrete, bulldozing, drilling bricklaying, welding, and demolition can be performed more efficiently by self-driving construction machinery than their human counterparts. Autonomous robots and machines are also increasingly being used for assembly-line and off-site construction wherein various components of buildings and structures like walls and floors are pieced together without the use of human labour. This not only saves time but also helps to free up human resources for other tasks.

Although AI reduces risks and increases cost-effectiveness of projects, it is unlikely to replace human labour. Instead, it will alter business models, reduce unnecessary expenditure and make construction projects more efficient.

E. Property Analysis, Lead Generation & Recommendations

Almost all home-buying and rental search engines recommend homes based on the nature of property (house, apartment, bungalow, condo etc.), price, carpet area, location and number of rooms. Although this model is sometimes effective, it suffers from two defects. Firstly, it does not provide customers with enough information regarding each offering; and secondly, it still leaves potential buyers with far more offerings than they are willing to consider. 

AI-powered software can be used for analysing various properties and their surroundings and disseminating accurate and comprehensive information about them to currents and potential purchasers, lessees, tenants and licensees. This information would include sales price/rent, ratings and reviews of local schools and colleges, a list of supermarkets, restaurants, pubs, bars, entertainment and recreational activities in the district, public transportation available, parking spaces, noise complaints, building violations, construction alerts, number of daylight hours per day/month/year etc. This information will enable real estate agents to attract the right kind of crowd to a particular property listing and to provide their clients with as much information as possible. It will also enable the clients to make a more informed decision regarding property investment. ‘Localize’ is an example of such a software that serves persons seeking to purchase or rent properties in New York and Chicago.

Self-learning AI algorithms can be utilized to generate recommendations based on the customer’s likes, dislikes and preferences, which can be gathered from past purchases, viewer history and search filters applied. ‘Trulia’ is a San Franciscan real estate marketplace network that furnishes its users with this kind of AI-powered personalization. The network uses computer vision to extract relevant information from the user’s photographs, such as preferred colour schemes and palettes, flooring and tiling, construction materials etc. The system then makes recommendations of listings based on the customer’s preferences. It also takes into account the preferences of other users that have looked at the same or similar properties. ‘Compass’, ‘Rex’ and ‘Redfin’ are other AI based platforms that provide similar services.

Intelligent chatbots can also be set up on property listing websites to collect leads, answer technical questions on square footage, lease terms etc. and provide virtual home tours to the potential homebuyers. ‘Apartment Ocean’ is an AI platform that provides real estate agencies with customisable chatbots that understand regional languages and that can answer and ask questions pertaining to various listed properties.

F. Real Estate Management

When it comes to larger properties like commercial and corporate office buildings, real estate maintenance is costly. Statistics show that upto 30-40% of office spaces remain underutilized and give rise to ‘silent costs’. In addition to the extra expenses incurred for energy consumption of the unused area, poor real estate management causes employee dissatisfaction.

TRIRIGA is an AI software solution that assists real estate managers to manage commercial and corporate office spaces more efficiently. The algorithm is designed to gather data that will enable organisations make better use of their workspaces. Natural language processing sensors encourage employees to talk to the spaces, which it autonomously then identifies as the employee’s needs. This data is relied upon for the purpose of rearranging and changing office layouts. 

‘Gridium’ is another AI-focused company, which specializes in optimising energy saving and property resource management. Machine learning technology helps to detect and analyse weather data and suspicious spikes in energy usage patterns to warn property managers. This enables building operators to react to issues on time and decrease operational costs.

Thus, proper real estate management help to create more appealing and productive workspaces that help to reduce operational costs and increases employee satisfaction and efficiency. 

G. Property Valuation

One of the most noteworthy features of AI is its ability to make reasonable predictions for the future. As the real estate sector involves high risks, the ability to predict outcomes is especially valuable. Enterprises that can anticipate rent and sales price fluctuations or identify the perfect timing to sell a property have an unprecedented competitive advantage over others. 

When determining the selling price or rental value of a property, property size, number of bedrooms, renovation quality, and other features are taken into consideration. However, luxury homes and villas are often custom-made with unconventional designs and therefore, are harder to evaluate. AI algorithms can combine current market data and public information such as crime rates, school ratings, buying trends and availability of transportation networks to make predictions of the property values. ‘Skyline AI’ is an Israeli start-up that uses predictive analysis to accurately assess property value from large data pools. 

‘Zillow’ is a San-Franciscan AI-based technology that analyses photographs in order to draw up property value estimates. These machine learning techniques can assess even the most sophisticated interior details and design. Together with the other typical price factors like area and size, Zillow’s algorithm is able to set the right price with a very low median error rate.

Whilst ascertaining the value of property, real estate agents may decide to take nearby restaurants, bars, coffee shops and grocery stores into consideration. This decision is usually based on their intuition and past experiences. AI, on the other hand, has an ability to find logical hidden and non-linear relationships between data and property desirability. These relationships can vary depending on the country, city and neighbourhood. 

H. Mortgage & Loan Performance Insights

Smart algorithms can provide mortgage and loan performance insights and can assist in mortgage calculations by reviewing mortgage applications, screening them for errors and completeness, assessing sources of income, calculating income and analysing key documents. ‘CoreLogic’ is an AI platform that assists real estate brokers and agents with checking documents, calculating income, underwriting loans etc. They also provide intuitive tools and services in respect of borrower risk, collateral valuation, portfolio monitoring, performance analysis, risk mitigation and default management.

Challenges & limitations

Although AI-enhanced operations aid growth and sales in the real estate sector, the technology itself is not devoid of challenges. The following are some of the limitations of AI that have to be taken into account in order to be able to bring out the best of AI in the sector:

1. 1. Difficulties with Data Collection: In case of UASs and drones, data collection is mostly done on-board. Limited internet connectivity, poor battery management and cloud cover make real-time streaming challenging. Further, temporal resolution of conventional sensors is encumbered by the limited availability of aircraft platforms and by the orbit characteristics of satellites.
   2. Disorganised Data Pools: AI algorithms are primarily fuelled by datasets and digitized information. Without organised and transparent data pools the algorithms would not be able to perform their functions and provide accurate predictions and insights. Several developing and underdeveloped countries have not yet embraced the digitisation age and are left with disorganised, unstructured and small data pools that can adversely affect the credibility of AI solutions.
   3. Reluctance towards Technology: In the past, automated property valuation models provided calculations and predictions with a higher margin of error. However, today, digitization and data organisation has significantly reduced the same. Some real estate agencies still fear the system and therefore, are reluctant to use and adopt these newer technologies especially for the purposes of property valuation.
   4. Contact Preference: Although our daily lives are technology driven, the usage distribution of technology is severely imbalanced. On one hand, some home buyers prefer dealing with an agent from the get-go and until completion of the transaction. However, on the other hand, some buyers may be open to using AI-powered technologies for at least some stages of the process if not for all.
   5. High Costs: In some cases, depending on the type and size of business, AI can prove to be costly. As AI is a machine learning software that constantly requires large and updated data pools, organisations may find themselves having to heavily invest in data storage solutions and hardware with high specifications to ensure that the software keeps running properly. Further, the AI technology we have today is in the early stages of development and organisations will require a system that can be constantly modified and updated with changing times and developments.
   6. Replacement of Human Labour: AI provides for the automation of several manual tasks and provides customers and homebuyers with direct access to research and data that is required by them to make an informed decision. Therefore, AI replaces the human resources that otherwise would have been employed or engaged to conduct research, check documents, provide home tours, formulate strategies etc. It makes repetitive jobs redundant and causes lay-offs and unemployment.
   7. Lack of Creativity: Although AI can replace human intelligence, it cannot completely replace human creativity. This means that it may not be able to solve problems that it is not completely familiar with and will not be useful to meet the needs of the changing times. In order to be able to keep learning, AI requires constant training, which again can prove to be costly. 
   8. Decision Making: AI is useful for data collection, organization and presentation, which facilitates better decision making. However, the software itself cannot make a final decision. Thus, one of the main challenges is that specialists are required to enable many of the automatic features of the algorithm and to take the final call.
   9. Cyber Crime: AI algorithms and software are predominantly used on online platforms and therefore, are susceptible to cyber security issues like hacker attacks, spyware, piracy etc. 
   10. Compliance with Legal Requirements: As AI evolves, it enhances the ability of the software and algorithms to use personal information in ways that can intrude on the privacy interests of individuals. Many countries have passed piracy and data protection laws that protect individuals from this. Therefore, the use of AI would have to be monitored and controlled in accordance with the statutory and legal requirements of each country.

Conclusion

From the discussion above, it is clear that the use of AI in the real estate sector gives rise to several benefits as well as drawbacks. On one hand, it is a time and cost saving tool that automates manual tasks and provides forecasts, projections and insights for the future. However, on the other hand, it can cause wide-spread unemployment; it lacks imagination to meet the changing needs of the market without continuous training and learning; and it has potential to be more costly than human resources. Thus, the costs of implementing an AI-powered software may outweigh the cost savings of using it.

Despite this, increasing modernisation, globalisation and digitisation have forged a new path where AI will play an increasingly important role in the real estate sector, in the years to come. For the time being, AI is unlikely to replace real estate lawyers, agencies and brokers. Instead, it shall provide them with a strong and secure foundation to grow their business operations. With access to predictive models, strategies and actionable insights, agencies will have better comprehension and perceptions in respect of properties, markets, and clients, which will help them gain a competitive advantage in the industry.

To obtain the perfect outcome in real estate operators, enterprises should analyse their organisational needs to determine the areas where AI would have the largest and smallest impact. This will enable them to create a collaborative model using both human capabilities and AI algorithms to establish the ideal balance between artificial and human intelligence, in the short and long run.

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