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Market demand forecasting

Introduction

Property industry together with other industries is changing due to technological developments. Some of the technologies which have been emerging in this industry include Machine Learning (ML) Artificial Intelligence (AI). In the realm where technology and real estate meet, there are many applications of ML AI, one of which is Market Demand Forecasting. It can be defined as the process of employing computational models that are based on learning techniques to estimate the future demand for properties or property prices, which can assist organisations in planning for pricing, advertising and development strategies. These technologies are already being adopted in the property industry and for good reason as it is becoming increasingly important in such a competitive market.

Challenges

The implementation of ML AI in market demand forecasting in the property industry has its share of obstacles despite the successes achieved. These challenges include; data quality and quantity, high costs of implementation, lack of understanding of the technology, and legal implications. This a challenge because the quality of data used has an impact on the accuracy of the AI and ML models. Sometimes the data is in an unorganized format, missing or inconsistent and this affects the accuracy of the predictions. Another challenge is the high costs of implementation especially to the small and medium enterprises. There is also the issue of complexity of the technology whereby many businesses have little or no knowledge on how to use it. Finally, there are regulatory constraints which may also be a problem since such technologies may not always be compliant with certain local or even national laws and regulations particularly in regard to data privacy.

AI Solutions

There are however, several AI solutions that can be employed in order to enhance the accuracy of market demand forecasting in the property industry. Some of these are predictive analytics, natural language processing, image recognition, and deep learning algorithms. Predictive analytics is a statistical method that is applied on past information to make future tendencies, and this can be helpful in an insight of property market demand. Natural language processing can be defined as the ability of a system to understand text in a human’s language; this means that the system can be able to capture social media posts, customer reviews and other online content to determine customer sentiment and possible demand. Image recognition can be applied to properties to establish characteristics that could influence demand like the location, design and state of the property. Deep learning algorithms, however, can work with vast amounts of data and are able to enhance their predictions with each iteration.

Benefits

There are a lot of advantages of applying ML AI in market demand forecasting especially in the property market. Firstly, it can enhance the efficiency of demand forecasting thus enhancing the pricing and marketing strategies. Secondly, it is possible to perform real-time analysis of trends in the market and adjust to the shifts in demand. Thirdly, it can assist companies to spot emerging markets since it can forecast trends and customers’ preferences. Finally, it can also help to minimize costs and enhance efficiency through the elimination of certain manual processes and the enhancement of decision-making.

Return on Investment

It is possible to get a high ROI when deploying ML AI in market demand forecasting in the property market. A study done by McKinsey & Company shows that organizations that apply AI in demand forecasting can cut down on forecasting errors by about 50%, while those that optimize on having the right products at the right time can minimize lost sales by between 35% and 65%. Furthermore, the same report shows that these companies can cut down on inventory expenses by as much as 20%. These numbers prove that there is a large return on investment for firms in the property sector that adopt ML AI for demand forecasting.

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