The Role of Business Intelligence in Housing Foresight

Tasnim News Agency - The housing market, as one of the strategic sectors of the country's economy, has always been of interest to policymakers, investors, and even the general public. The increase in urban population, land restrictions, economic fluctuations, inflation, and climate change are among the most important factors that have made the future of this market complex and unpredictable. In such circumstances, the use of scientific tools to analyze data and predict trends is an undeniable necessity. One of the most important of these tools is "business intelligence," which facilitates forward-looking decision-making by combining information technology and data science.

Business intelligence is a process that ranges from collecting raw data to advanced analysis and producing analytical reports. In the housing sector, data can include transaction prices, the number of building permits, population growth rates, household purchasing power, employment status, bank interest rates, and even geographic and climatic data. The aggregation and analysis of this data helps policymakers identify hidden market patterns and make more effective decisions.

Today, many countries have been able to better control supply and demand by creating national housing data banks. For example:

By combining economic data and machine learning algorithms, China applies policies restricting buying and selling only in cities that are likely to jump in prices, thus preventing unreasonable market stagnation in other cities.

By using predictive models based on business intelligence, Germany has determined construction capacities in each state in proportion to population growth and immigration, preventing housing shortages or surpluses.

In addition to the macro level, business intelligence is also effective at the level of small and medium-sized businesses. Construction companies can allocate their capital more optimally by analyzing buyer behavior and identifying real market needs (for example, the demand for small or luxury units). Even banks and financial institutions can provide smarter construction facilities by analyzing economic and risk data and prevent financial crises similar to the 2008 housing crisis.

One of the key advantages of business intelligence is data integration. In many countries, housing-related data is scattered and heterogeneous, which causes decisions to be made with delays or based on incomplete information. Business intelligence can collect diverse data from different organizations (land registry, municipalities, banks, statistics center, etc.) into a centralized dashboard and provide real-time analysis to decision makers.

In addition, the development of interactive dashboards and simulation models also plays an important role in foresight. Managers can simulate different economic scenarios and predict their possible effects on the housing market. For example, we can examine how an increase in bank interest rates will reduce demand for housing or how the implementation of a large development project on the outskirts of the city will affect land prices in the area.

From a social perspective, the use of business intelligence can help distribute resources more equitably. By accurately identifying areas with a housing shortage, governments can implement more targeted support policies such as providing low-cost loans or allocating cheap land for mass construction. This reduces the class gap and increases public satisfaction.

The lack of business intelligence in the housing market means making decisions without accurate information and based on speculation. In such circumstances, governments and private sector actors will not be able to correctly predict supply and demand trends; as a result, the likelihood of price bubbles, recessions, or even overconstruction in some areas increases sharply. This can lead to a waste of financial resources, public dissatisfaction, and reduced investor confidence; Similar to the 2008 housing crisis, which was caused by incomplete analysis and lack of accurate warnings.

On the other hand, the use of business intelligence makes the housing market more transparent, stable and predictable. Real-time data analysis allows for the rapid identification of high-risk areas in terms of irrational price increases and allows the government to adopt timely corrective policies. Also, investors can enter construction projects with more confidence because economic risk is reduced and financial resources are directed towards projects that are in line with the real needs of society. This approach not only stabilizes prices and prevents sudden shocks, but also improves social welfare and leads to sustainable urban development.

Today, when data is produced at a high speed, ignoring its intelligent analysis means blind decision-making. Business intelligence can draw a clearer and more accurate roadmap for the future of the housing market. It is recommended that countries take advantage of the opportunities to make the housing market more predictable by investing in data infrastructure, training data analysis specialists, and developing business intelligence systems. The result of such measures will be the formation of a stable market, reducing price fluctuations, and increasing public trust.

*Seyed Mohsen Tabatabaei Mozdabadi, faculty member, Azad University

Tasnim News Agency