Samma Keetsin

Director and Independent Director, Sena Development Public Company Limited

Currently, there is a vast amount of information and analysis regarding real estate being disseminated and shared in various forms on Social Media from real estate research agencies, real estate brokers, self-proclaimed experts or gurus, and analysts from other organizations who gather information either through interviews with these operators or by copying from one another without ongoing analysis. There is no verification of the data or the content of the analysis to determine if it is reasonable or if the analysis aligns with the data.

The data or analysis that the author finds to be well-prepared often comes from stock analysts in the real estate sector who have the opportunity to meet and discuss details with operators or from their ability to thoroughly examine financial statements and performance, allowing for systematic comparisons with both the industry and historical performance.

However, general real estate data and analysis still raise many concerns that operators, academics, journalists, or consumers who intend to disseminate this information should be cautious about.

The body of knowledge consists of data, information, knowledge, and wisdom.

Regarding data related to the housing sector, it is generally scattered, both directly and indirectly related. Directly related data includes construction data, housing sales, demand and supply of housing, housing loans, etc. Indirectly related data includes urban population growth, structural changes in demographics, public transport services, urban development, economic data, especially household debt, interest rate trends, etc.

If we take this scattered data (whether from the same source or multiple sources) and create relationships among the data, it will generate information that can be utilized for analysis to create knowledge. When recipients of this information apply it, they can generate wisdom to use in their own context or organization.

The most important thing is that each step must be accurate and reliable in itself to ensure that subsequent steps are likely to be correct as well. Especially if errors occur from the first step, such as incorrect data, then information, knowledge, and wisdom will also be distorted.

There are many reasons that can lead to incorrect data, such as entering or keying in incorrect information into the system (human error), receiving data that the provider intentionally falsified, or receiving data from providers who misunderstand the questions or lack sufficient options to respond (often seen in political polls). The solution is to exercise caution in verifying accuracy and to use judgment and experience to consider possibilities.

Even when the data is correct, establishing relationships, such as linking data into meaningful tables, may create perspectives that the original data provider did not establish. If they did create it, it may not be usable because it was not formatted in a table like Excel or was insufficient. Data recipients must organize it to connect it in a beneficial context, such as having population figures by area but not comparing them year-on-year in the same area, or having housing sales figures compared to the previous year but not showing historical comparisons over the past 3, 5, or 10 years. If research agencies do not present trends over the medium or long term, there is a risk that the information disseminated may not reflect significant realities due to abnormal events during the comparison period.

In another case, providing information without accompanying notes or using insufficiently detailed titles or headings may lead recipients to misunderstand. Some research agencies present sales rates of housing projects only for the first month of sales, which may lead recipients to believe it represents sales for the entire year. Meanwhile, some agencies do not clarify that the data being linked comes from different databases, with some being new housing data only, while others include all housing data, both new and second-hand, making direct analysis impossible. The solution is to pay attention to providing sufficient details for users.

When moving from information to the stage of writing analyses for dissemination to provide knowledge, it often appears that the analysis does not reflect the data or information presented earlier or analyzes things that do not originate from the data, or predominantly analyzes subsets (fallacy), or in the worst case, may analyze in contradiction to the data and information presented (misinterpretation).

As for wisdom, it is the unique component of the recipient of data, information, and knowledge, determining how it can be applied beneficially, as each individual and organization has different internal factors, leading to potentially different perspectives.

All of this presents a challenge for consumers of real estate news to understand, as many research agencies often provide information, data, and analyses in different directions due to varying scopes of data, timeframes, definitions of each data item, updates, and the reliability of the data. Even the same research agency may provide conflicting information, data, and analyses. The best approach is to familiarize oneself with the presentation styles of each research agency, to be someone who does not easily believe anything (inquisitive nature), and if sufficiently knowledgeable, to analyze that data or information independently across multiple dimensions.