Data vs Real estate – Predictive analytics using Big Data for the real estate.

When I first studied real estate and started investing, I was obsessed with reducing the likelihood of failure. I’ve been looking for a mysterious medicine that can eliminate risks in investment activities that necessarily involve high risks. Big data is what came to me like fate when I found a famous medicine. They use big data to invest in real estate that can rise and sell real estate that will fall. Being completely persuaded, I drank a masterpiece of medicine. Today, I’d like to talk about whether the investment was really effective and whether the real estate big data that I drank can really meet the future.
Big data and real estate.
Big data refers to the act of processing large amounts of data to derive necessary information. This is because technological advances have made it possible to collect and analyze large-scale information. In real estate, there are many attempts to collect all relevant data, process it in a good form, and further guess the future. Representatively, there are sites such as real estate magazines, Acyl, and Hogangnono.

This is Richigo’s apartment investment score analysis.

This is Darwin Jung’s reconstruction score.
Richigo, which provides future prospects and investment scores beyond information provision, and professional real estate prop-tech companies such as Darwin Brokerage, which express the possibility of reconstruction apartments with scores, are also emerging.
The beginning of big data real estate investment.
When I started studying real estate, I read a lot of REM’s books. I learned and became interested in running a real estate big data system called zip4. Then, at the bookstore, I recently bought Richigo Kim Ki-won’s big data real estate, which is famous for being a downwardist. And I paid attention to the real estate of Daejeon, Seoul, and Jeonnam, which appear as promising investment areas in this book.

Big data real estate investment.
Kim Kiwon.
Dasan Books.
200 percent of the predictions of this book were correct. Housing prices in Daejeon showed a high increase in 2018 and 2019.

This is the 2018 and 2019 sale price of Daejeon. (Korea Appraisal Board)
And I became a believer in big data investment and after that, I took a lot of big data-based investment lectures. Kim Ki-won took not only a special lecture on real estate but also a lecture by CEO Park Sang-yong. CEO Pl also has a great hit rate.

Manager Park said he invested in real estate with big data. So, how much did he earn?
Park Sangyong.
I also became a believer in big data investment and studied hard. However, as I did it, there were many advantages, but the disadvantages began to be visible.
Advantages of big data investment.
The biggest advantage of real estate investment through big data is that you can buy undervalued areas. In other words, I think safe investment is possible. If you invest in real estate using big data, it will help you choose areas with low supply, low housing prices to income, high jeonse rates, and little unsold. It is difficult to fall significantly even if it falls, and it is highly likely to rise significantly if the upward wind blows due to increased liquidity like recently. In particular, the rate of return itself is very high because it targets low jeonse rates. Compared to stocks, it’s very similar to the value investment method.

Disadvantages of investing in big data.
As it is very similar to the stock value investment method, the disadvantages are similar. Local products are undervalued more than areas that grow rapidly or want to buy. This is because local real estate has low speculative demand, so there are many areas with less bubbles or rather undervalued. The problem is that it’s not an area where you can easily reach out. Kim Ki-won’s big data real estate investment network, published in 2021, selected Wonju, Seosan, and Gunsan as promising areas. It was a huge hit, but it’s not an area that you can easily reach unless you’re a professional investor.

Big data tells you that this year’s butler is a good area.
Big data tells you about this year’s butler area [Jip Economy TV], House Interview/ Kim Ki-won, CEO of Data Nose, Part 2.
It may also be accepted as a sign for those who think of living in real life, not for the purpose of real estate investment, to continue to delay purchasing homes. This is because it is an overheating that cannot be explained in big data. Representatively, Kim Ki-won, CEO of Richigo, who insisted on overheating real estate in Gyeonggi-do, Seoul, has said that since 2020. In 2021, housing prices in Gyeonggi-do, Seoul, soared unprecedentedly. Just as Tesla soared despite experts’ ridicule of overheating, real estate is also possible.

The use of real estate big data is…
Based on the undervalued area,
Personally, I think anyone who thinks of investing in real estate should use it. In fact, everyone is smart these days, so I don’t think there’s anyone who doesn’t invest. Strategies to refrain from investing in highly valued areas and invest in undervalued areas within financial capacity can increase stability.

This is Richigo’s future prospect. Don’t trust me and just refer to it.
However, investments should not overconfidence in big data to predict the future. I think it’s dangerous to sell at a high point and buy at a low point. Big data is a data that helps judge undervalued areas, not a witch’s crystal mirror that fits the future. In fact, even undervalued areas cannot know when they will rise due to big data. Even in highly valued areas, we don’t know when it will fall. In 2020, it was said that housing prices are higher than interest rates and housing prices are higher than jeonse prices due to big data. However, interest rates were lowered in 2020 and jeonse prices were raised to the three lease laws. In the end, housing prices felt cheaper compared to interest rates, and jeonse rates rose, creating an environment favorable for gap investment and rising significantly. In other words, the future outlook is that it is barely possible to meet the current situation only when it does not change at all.
Big data is a vast collection and organization of past data. You can’t guess the front by looking at the rearview mirror. Even if there are similar cases in the past, it is not necessarily repeated. Big data should only be used to determine undervalued areas. Please remember that attempts to catch both low and high points, whether in stocks or real estate, always fail.

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