Estimating property prices with advanced machine learning techniques makes it possible to significantly improve the marketing strategies of companies in the real estate industry.
Real estate markets are influenced by complex economic, social and political factors, and must adapt to changing market conditions by considering uncertainty as a major component of risk assessment.
Establishing the necessary strategies to monitor and respond to potential threats is critical to anticipate the future, so having information that facilitates tangible business decisions is strategic for organizations.
Data to estimate the right price
Real estate datasets are often heterogeneous, incomplete and dispersed, so ensuring data quality and consistency is crucial to the effectiveness of machine learning models. Therefore, it is necessary to create a data management strategy that makes it possible to define and align the vision, goals and actions of the company.
Thus, determining the market value of properties offered for sale or rent through factors such as amenities, location, size, orientation, services or the presence of green spaces plays a crucial role in setting the right price for a home.
Currently, technological development and the capacity for continuous data collection make it possible to implement data-driven technological solutions based on advanced machine learning techniques, which facilitate estimating the target price for sale or rent based on the incremental factors that affect the price of a property. According to the data analyzed, and taking into account the great importance of the local level of marketing in this industry, it can be said that more than 50% of the current price of a property comes from the cost of land and taxes, especially at a time of high demand.
View data
| % of price | |
|---|---|
| Rooms and size | +33% |
| Baseline | +30% |
| Amenities | +9% |
| Height | +4% |
| Interest rate | +3% |
| Orientation | 0% |
| Distance to center | −5% |
| District or zone | −15% |
Adapting the offer to the buyer profile
Companies must learn to adapt their growth strategies by identifying needs according to the profile of potential buyers in order to personalize and discriminate the commercial offer, thus shortening the purchasing process.
Undoubtedly, the financial impact of the time inventory remains on the market is one of the main factors that can influence the selling price, especially in a scenario of higher and longer interest rates such as the current one.
The implementation of machine learning algorithms makes it possible to identify behavioral patterns that facilitate more accurate and faster pricing, which increases the speed of response by companies throughout the sales process.
In this case, the sales price adjustment reached +8.63% using neural networks, since the algorithms learn continuously in a short time.
In addition, considering another fundamental factor in price estimation, such as adapting the offer to the characteristics of the properties, will make it possible to establish commercial strategies with a higher level of personalization, which leads to a better response on the part of the demand.
View data
| Profitability | % of inventory | % of revenue | |
|---|---|---|---|
| Type 1 | 1.15 | 4.5% | 5.2% |
| Type 2 | 1.12 | 8.7% | 9.7% |
| Type 3 | 0.97 | 14.9% | 14.5% |
| Type 4 | 1.16 | 16.9% | 19.5% |
| Type 5 | 0.66 | 26.1% | 17.3% |
| Type 6 | 1.17 | 28.9% | 33.8% |
Indeed, by applying machine learning techniques to classify supply and demand, it is possible to improve profitability rates by up to +17.0%, depending on the type of inventory; likewise, by identifying the least profitable property segments, companies can strategically prioritize resources on selling the products with the highest yield potential.
More agile purchasing processes
Therefore, the implementation of machine learning techniques in the real estate industry allows companies to adapt to more agile purchasing processes, something that occurs in all industries. To do this, continuous flows of quality data must be established to feed the databases of both supply and demand in real time, and to improve the quality and accuracy of the price estimation algorithms.
In addition, identifying potential buyer segments based on their real estate interests will allow the development of contact strategies to optimize revenue and efficiency based on supply and demand. This will allow companies to focus on marketing the properties that maximize profitability and strengthen revenue growth strategies.
Moments of high market demand often put upward pressure on prices, which can make it difficult to identify the overheating risks facing the industry. Undoubtedly, the implementation of technology and good data management may open up new growth opportunities for companies in the real estate industry.
