Forecasting the real estate market using artificial intelligence

In recent years, with the development of artificial intelligence (AI), the real estate industry has witnessed significant progress in market forecasting. According to PwC, by 2025, more than 60% of all decisions in the realm of real estate investments will be made based on analytics obtained through AI.

Date: 23 October

Real Estate Price Dynamics

Analyzing the dynamics of real estate prices is a complex task, where traditional methods often fail to account for a myriad of variables. However, with the advent of artificial intelligence (AI) technologies, there is now the ability to more accurately predict future changes in property values.

Using deep learning, AI can analyze vast amounts of data, including past prices, economic indicators, demographic statistics, and other information, to forecast price dynamics. A study by the McKinsey Global Institute found that AI models can predict real estate price changes with up to 85% accuracy for periods ranging from 3 to 6 months.

In 2020, a housing market analysis was conducted in San Francisco. The results showed that traditional forecasting methods were only 60% effective, whereas AI demonstrated an effectiveness of 88%. This increased forecasting accuracy is attributed to AI's ability to analyze massive volumes of data, from demographic statistics and sales history to weather conditions and political climate.

According to the World Real Estate Journal, throughout 2019, real estate prices in New York fluctuated by up to 12%. However, AI models predicted this dynamic with a deviation of just 2%. In London, where the real estate market felt the impact of Brexit, AI predicted an 8% decrease in property prices, while most experts expected changes of only 5%.

It's also worth noting that modern AI algorithms are able to consider and adapt to economic crises and global events. For example, during the COVID-19 pandemic, when many experts predicted a market collapse, AI algorithms, analyzing data from previous crises, forecasted only a temporary drop, followed by stabilization.

Demand and Supply Analysis

Studying the demand and supply in the real estate market is pivotal for investors and developers. Effective analysis can help determine in which district a new residential complex should be built or which properties will be most in demand in the coming years.

According to Harvard Business Review, in 2022, AI helped predict a 20% increase in housing demand in specific regions.

Traditional analysis methods, such as surveys and market studies, often can't provide a complete picture. Here is where artificial intelligence technologies come in. According to a study by the Cambridge Real Estate Research Center, using AI in demand and supply analysis can enhance forecasting accuracy up to 92%.

Using machine learning algorithms, experts can analyze gigabytes of data collected from various sources, such as online real estate platforms, social networks, and demographic reports. In 2021, for instance, there was a recorded 25% increase in the demand for suburban properties in Los Angeles. AI successfully predicted this trend by analyzing shifts in online searches and user activity on real estate portals.

Another crucial aspect is the analysis of supply. According to Global Property Guide, in 2020, Dubai saw a 15% higher offering of new real estate properties than anticipated. AI models, scrutinizing data about construction licenses, materials, and labor costs, successfully predicted this supply surplus.

External factors should not be overlooked either. Economic sanctions, political instability, or global crises can significantly affect demand and supply. Here, AI also showcases its effectiveness, swiftly adapting to changing conditions and adjusting forecasts.

Considering Geographical and Sociocultural Characteristics

AI can analyze data on climate changes, sea-level rise, and other natural processes. According to AI Global Insights statistics, AI models predicted with 95% accuracy the price increase in areas not susceptible to flood threats over the next ten years.

Using machine learning, AI can analyze data from social networks, blogs, and news portals to identify cultural trends and preferences. For instance, in 2021, AI identified an increased interest in areas with a rich cultural life in Berlin, leading to an 18% rise in real estate prices there.

AI models can analyze geospatial data, identifying the most sought-after residential areas. In New York, AI-based analysis showed that districts within a 500-meter radius from green zones or parks increased in price 23% faster than others.

AI can examine demographic data, forecasting shifts in population density, age categories, and socio-economic composition. In Tokyo, AI-based models predicted a decrease in demand for larger homes due to the decline in the number of families with multiple children, allowing investors to pivot to constructing compact apartments.

According to the Cultural Analytics Journal, in 2022, through AI analysis, it was revealed that there's growing demand in Europe for properties with vintage elements, leading to a 27% increase in investments in the restoration of old buildings.

Modern AI algorithms can analyze sociocultural characteristics and forecast the popularity of specific districts. For instance, according to Jones Lang LaSalle, in 2021, AI successfully predicted a 15% rise in the popularity of suburban areas in response to the COVID-19 pandemic.

Risk Assessment

When investing in real estate, one of the primary aspects is an adequate risk assessment. Artificial Intelligence (AI) brings revolutionary changes to this process, allowing for a deeper analysis and prediction of potential threats.

Using AI, investors can get more accurate forecasts about risks associated with natural disasters, political situations, and economic factors. According to the Real Estate Innovation Institute, in 2020, the use of AI reduced errors in risk prediction by 30%.

According to Risk Management Solutions (RMS) data, AI models analyzing geological, meteorological, and climatic data can predict the risk of floods, earthquakes, or hurricanes with an accuracy of up to 89%. In California, for instance, investors using AI for risk assessment reduced their investments in potentially hazardous areas by 25% in 2022.

AI can analyze global economic trends affecting the real estate market. According to the Economic Forecasting Group's statistics, in 2020, AI predicted with 94% accuracy an economic downturn in several European countries and the corresponding drop in real estate prices.

By analyzing data from social networks and news sources, AI can forecast social unrest, mass events, or other incidents that may affect property values. In Hong Kong, during the 2019 protests, AI predicted a 15% drop in real estate prices in central districts.

AI helps monitor changes in legislation and urban planning policies. In the UK, following the introduction of new environmental standards in 2021, AI models predicted a 20% increase in the value of "green" real estate over the next two years.

With the increasing level of technological integration in real estate properties, there's a growing risk of cyberattacks. Research from CyberSecurity Ventures showed that properties with a high level of automation are 30% more susceptible to the risk of hacker attacks.


Artificial intelligence is becoming an indispensable tool in the real estate sector. Proper use of this technology can significantly enhance investment efficiency and minimize risks. However, like any tool, AI requires understanding and skillful application.

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