The Art of Collecting and Analyzing Real Estate Data

Knowledge is power. Humans have known this for a long time, and the data revolution the world is currently in is proof that the saying is right. Some of the most successful companies today have proven capable of employing data most effectively. This is true in every industry, and the same goes for real estate.

The real estate market is particularly tricky. Fluctuating markets and rapidly evolving trends can create a market where opportunities can appear and disappear rapidly. Strategies that were profitable a few years ago might be ineffective in a few years due to saturation, rising market disinterest in a region, or events as unexpected as natural disasters. As such, the smartest investors rely on data to guide their intuitions.

That said, the first step to obtaining actionable real estate information is to collect and analyze raw data. The more the data, the more accurate the insight.

Therefore, with the right volume of quality data, a great understanding of market dynamics within the industry, and a reliable real estate scraper, success in real estate is more attainable than ever.

Why Businesses Require High-Quality Real Estate Data

The biggest brokers, developers, investors, and owners utilize real estate data to facilitate decision-making. Insights from the data they obtain provide them with a more objective view of future profitability and value. By extension, this helps inform their decisions to buy or sell at a particular price or time. Quality real estate data also plays at the negotiating table.

Sources of quality real estate data can vary from country to country, but they usually include government census data and resources that detail the distribution of businesses and resources.

However, the type of data collected that could influence the quality of a property is relatively more uniform regardless of location. Examples of useful real estate data are:

• Distribution of businesses
• Reviews of businesses
• Market value of surrounding property
• Occupancy rate and rental fees of surrounding properties
• Daily/weekly traffic in the area of interest
• Crime statistics and trends
• Demographic distribution within the area

The type of property and the intent behind its acquisition would determine which data points are relevant. That said, businesses or agents could determine how valuable or suitable a property is for a particular purpose. For instance, the shopping trends and business distribution in a region could indicate the suitability of the region for a new mall.

What is The Best Way to Obtain Quality Real Estate Data?

The best and most convenient way to obtain data for real estate analysis is to scrape it from online sources. However, not all real estate data is readily available online, and some good old-fashioned research work may be necessary. However, the data available online are collectible using a real estate scraper.

Depending on location, businesses may also need location proxies to facilitate the process, but more often than not, a reliable scraper is enough to extract the relevant data from the census bureau, county assessor, and property listing websites. Other useful sources of data are Google Maps or similar applications, online review aggregators, etc.

All of these are scrapable with a real estate scraper (especially property listing websites). The scraper is capable of parsing the contents of the website to identify the relevant data within. Upon identification, the scraper extracts the data and downloads it to a local file for storage and later analysis.

Limitations of Web Scraping for Real Estate

Web scrapers are handy, but they have their limitations as well. Some of these limitations are:

• Yes, the frustrating puzzle or image you have to sort through before gaining access to a website is an obstacle for scrapers. In fact, they exist specifically to stop bots like scrapers.
• Honeypot traps. These web elements are invisible to humans but are visible to the web scrapers. As such, any action that indicates that the element or object is identifiable theoretically exposes the activity of a scraper bot.
• Behavioral detection systems. Some websites employ systems that seek to identify non-human browsing behavior. This is arguably the most difficult anti-scraping defense to beat.

The above limitations are valid for all web scrapers, but vendors now create ready-to-use real estate scraper APIs that are capable of evading both honeypot traps and beating captchas.

In other words, websites are unlikely to detect these specialized ready-to-use APIs. The distinction between these APIs and other generic web scrapers is worth paying for because the consequence of a detected scraper is an IP block and indefinite restriction of access.

Conclusions

The dynamic nature of the real estate market means that many factors are influencing the value of a property at any point in time. Businesses looking to be profitable, therefore, need to be aware of these factors and how they interact with one another to determine value. Only then can they make intelligent decisions about a property.

Fortunately, the data is available for the willing to obtain. By mastering the relevant market factors, along with real estate data collection and analysis, businesses can make success more likely than not.