Reply To: How can you predict the future, surely no one can do that?

Home Forums Property Market Research Q&A How can you predict the future, surely no one can do that? Reply To: How can you predict the future, surely no one can do that?

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Avatarinvestingme
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I found this discussion very interesting. Alex, I believe that the cyclicity of the property market would still remain even if there is a high adoption of data driven decision making. This is because, cyclicity of the market does not only depend on the sentimentality of investors/buyers but on many other factors such as the availability of capital and economic market conditions. For example, data might show that a particular area will definitely boom but all investors might not be able to ‘flood’ the market due to their budget restrictions. Even if they could, data would take that into account meaning that it might discount the same area due to the resulting overpricing of properties and subsequently highlight a boom elsewhere. This would support the general consensus that Australia is not one property markets, but is comprised of many different submarkets which would engender cyclicity due to the movement of investments and changes in preferences.

As such, what I found particularly interesting about the self-fulfilling prophecy element of data driven investing which does not eradicate market cyclicity is highlighted in the following section from MacKenzie and Millo (2003). Although lengthy, I think it is essential to quote it in its entirety given preceding discussions:

“There are few organizational environments as tumultuous as that of the financial derivatives trading. In 1973, the economists Black and Scholes tried to understand how options were priced in the newly instituted Chicago Board Options Exchange. They developed a formula that priced options (i.e., the ability to purchase a particular commodity at a specific price) based on a handful of parameters like its spot price or time to maturity. In the first months of trading, the formula failed to predict option prices, with a typical deviation as large as 30% to 40%. Yet, over time, the formula became accurate to within the low single digits, leading its creators to receive Nobel Prizes. Interestingly enough, however, the formula became accurate only because market participants acted as if it was accurate. Traders used values derived from the formula to inform their bids, the formula became integrated into trading regulations, and assumed in technological infrastructure. The Black-Scholes formula began by modeling option prices. It ended up modeling option prices in response to its modeling of option prices”

Looking at your service provision page, you claim that your prediction algorithm has also a low digit margin for error for high confidence properties. I can only assume that as the entire economy shifts to a data economy, your model will increase in popularity and precision.