Why Ignoring Market Data in Real Estate Will Cost You Sales

For being successful in real estate, the agents have to keep abreast with a lot of info. Clients will look up to you for all facets of the transaction. Understanding the data and using it can be the differentiator and it doesn’t have to be tough. Essentially there are six data points all the agents have to be aware of when they are representing a seller or a buyer. They are the absorption rate, average days spent in the market, months of inventory, list and sell price ratio, appreciation rates, and the average cost per sq feet. With this market data in hand, you need to ask yourself about the pattern you can observe emerging from it.

Importance of monitoring the market

Keep in mind that real estate is a business and it must be treated that way. Having a business-driven mindset means you quantify the data as far as possible. This can provide lagging and leading indicators about where the real estate market is heading towards. If you fail to monitor this market data, it might mean providing erroneous advice to your clients. Some of the metrics that may have a tangible impact on the home pricing are interest rate trends, commission rates, and construction costs in the area. Being aware of the current commission rates helps your marketing effort to be more competitive.

Keep track of the larger picture

After the agents have a good grasp of the local inventory and market data, you can expand the analysis further. You need to keep on monitoring the trends from as many different angles as possible. Consider demographics, marketing, global challenges, interest rates, and economic disruptors and how they interplay with one another. The five significant areas to concentrate on are,

• Demographics: The question could be how the income levels and age of the audience are shifting and how it is going to affect the market? Do the marketing tools I have to align with the demographics? For instance, do I need to change something to appeal to Gen-Z or Millennial buyers?
• Consumer resources: How many new job vacancies are created about the new housing starts? How is this going to affect the supply and demand?
• Employment data: Where do the clientele find their real estate agents? You need to align your marketing efforts to reflect the different resources, being used by the consumers to connect with the agents.
• Movement: What is the number of people moving in or out of the market area? Where are these people moving to and from? If you are aware of this info you can tap into the referral networks from significant feeder markets to enable these moves.
• Buyer trends: What are the things a buyer is looking for within a home? How does this demand shift over a while?

You need to evaluate competitive threats, trends, and newer business models in the real estate sector that will affect the business. You also need to keep an eye on future trends in the real estate sector, for guiding and informing the clients. You can use tools such as Survey Swap for this.

Identifying the trusted market data sources

If it appears as if you will be needed to unpack a great deal of info, don’t worry. The significant thing here is collecting reliable resources that you can turn to regularly. You can use the data collected by reliable local appraisers, demographic tools, and different automated valuation models. Find out about various reports that are addressing your market. Never be afraid of a lot of data. It can become your advocate. It legitimizes the info you share and provides credibility as an advisor. You will come out as an expert that can be trusted during a purchase or sale of real estate.

Conclusion

Several real estate companies have been making their decisions based on a blend of traditional and retrospective data and intuition. Nowadays there are a host of new variables you need to consider. These variables also allow you to paint a more vivid picture of the future risks and opportunities of a location. One of the ways of using market data via advanced analytics is by using machine learning. It becomes easier to interpret disparate sources by using machine learning algorithms.