Data Science For Valuations

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Why data science is becoming increasingly important for the valuation of real estate

Over the years, an unprecedented amount of data about the real estate market has been collected. All this information about historical transactions, value developments and market trends provides a wealth of insights with which to better substantiate the value of real estate. Real estate investors can also make a more accurate estimate of the expected return thanks to big data and data analyzes. But how do you successfully translate this data into concrete investment choices for your real estate portfolio?

Many investors prefer a tangible investment such as real estate to stocks or bonds, or want real estate to have a substantial weighting in the total investment portfolio. In addition, investing in real estate can be very lucrative and offer a good return.

One of the challenges involved is the valuation of individual real estate objects in particular. When owning multiple shares in one company, all shares are mutual

the same and move with the course. For investment properties, each property is unique and many more variables influence its value. Even when an investor has several apartments within one building, the valuation per apartment can differ materially.

Big data makes it possible to streamline all variables and thus arrive at a better valuation for a complete real estate portfolio.

Data science for the investment market

KPMG research shows that more and more real estate companies and large investment companies use big data to make strategic investment decisions. Analyzing all data from the real estate market in order to arrive at valuable insights, however, requires expertise and smart calculation models.

The use of computer-controlled data science has long been an important trend in other investment markets. For example, artificial intelligence is increasingly being used for trading on the stock exchanges. Based on computer-controlled data analyzes and calculation models, investment decisions are made in less than seconds. No human is involved anymore in that process. The big question is whether the real estate market will undergo the same development and what role big data will play in this.

Alignment of variables

The valuation of individual real estate objects depends on several variables, which makes each object unique. Consider important characteristics such as location, year of construction, destination, size and construction. Moreover, more and more variables have been added over the years, such as sustainability and energy efficiency. How can these variables be aligned in order to determine important trends and value developments based on data analysis? There are currently a number of methods .

In countries such as Singapore, for example, a “Hedonic Regression” technique is used. With this method, important property characteristics and variables are individually valued. Real estate objects are subdivided on the basis of these characteristics and associated value.

In the United States, the so-called “repeat sales” method is often used. Here the focus is on the historical transactions of a real estate object in order to determine the current value.

In addition to these two methods, the value of multiple real estate objects is also increasingly determined on the basis of zip code or the historical value development of an entire neighborhood or region.

Automated valuation tools

Just like on the stock exchange, real estate trading is also becoming more and more automated. For this, “Automated Valutation Tools” are used that mainly work on the basis of current statistics. This concerns, for example, websites with which a value estimate for a real estate object can be obtained immediately.

Practical examples include the US website Zillow , Urbanzoom in Singapore and the Finnish Skenariolabs . It is expected that the technology behind these tools will develop further, with which the value of real estate can be determined even more accurately.

Such tools can also help real estate investors to make a good estimate of the current value. In the United States, there are now even websites and tools that also fully automate the bidding process. An example of this is the tool of the website Opendoor . Data and information from such valuation tools n also commonly used by mortgage providers, insurers and other financial institutions.

Predicting the real estate market

Data science gives real estate investors access to data on historical transactions and current valuations of individual real estate objects. But what about estimating the expected return? Data science also offers a possible solution for this. For this, roughly two methods of data analysis can be distinguished.

The simplified method is to collect and analyze statistics about the local housing market, where the property is located. This may concern information about the development of the average real estate value within a region, but also the development of the WOZ value. Using the Autoregressive Integrated Moving Average model, trends and patterns can be determined based on this information. Subsequently, an estimate can be made of the value development of real estate in the short term.

The second method for predicting the real estate market is more extensive and complex, but therefore also more accurate and provides a better view of the long term. In this case, the value development of the local real estate market is not only taken into account. It also looks at the development of demographic characteristics of the region in question, the development of the average income, unemployment figures up to the larger macroeconomic variables. This method is based on the principle that ultimately all global economic developments can influence the value development of real estate. By using the “Vector Autoregression” and “Vector Error Correction” model, a good forecast of the real estate market can be made based on this data.

The value of data science

The use of data science in the real estate market is still in its infancy, but could take off in the coming years. More and more real estate investors will have direct or indirect access to enormous amounts of data and will make decisions based on it. Are you having your real estate portfolio managed by an external party? Find out what role big data and data analysis currently play in making investment decisions and how they will deal with this in the future.

Despite these developments, the real estate market also remains vulnerable to unpredictable events that models and statistics cannot take into account. Therefore, use data analysis mainly to substantiate developed hypotheses.[/fusion_text][/fusion_builder_column][/fusion_builder_row][/fusion_builder_container]

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