| Pub. Date | : Apr, 2022 |
|---|---|
| Product Name | : The IUP Journal of Applied Economics |
| Product Type | : Article |
| Product Code | : IJAE40422 |
| Author Name | : Sanjay Fuloria |
| Availability | : YES |
| Subject/Domain | : Economics |
| Download Format | : PDF Format |
| No. of Pages | : 8 |
A lot of data is available for research today from a plethora of sources. There is a need to generate insights from this horde of data, and a lot of times the traditional techniques of analysis might not be suitable for making any fruitful decisions. Hence, there is a need to apply techniques that are available.
Machine learning is a new technique which can be applied successfully to huge datasets to generate insights (Ngiam and Khor, 2019). While understanding the use of machine learning, we will do well to know about the two cultures prevalent. One culture about the use of statistical modeling assumes that stochastic data models generate data. On the other hand, there are people who assume the data mechanism to be unknown. They use algorithmic models (Breiman, 2001). There are suggestions in the literature to use tools other than data models. Machine learning discovers patterns in data, it does not overfit. It does better out-of-sample forecasting (Mullainathan and Spiess, 2017).
The importance of housing in a country's economy cannot be overemphasized. The value of a country's currency is directly related to the strength of its property market. The economic impact caused by the new house supply is evident in the purchase of items such as furniture and household equipment by homeowners and the procurement of raw materials by homebuilders or contractors to build houses. In addition, customers have the resources to make significant investments, and the robustness of the construction industry is demonstrated by the abundance of available housing (Temur et al., 2019). Housing is important as per a number of international organizations and human rights groups. Each country's economic, financial, and political structure is intrinsically linked to the structure of the house. House prices have always fluctuated, posing a problem to homeowners, building owners, and real estate investors. In several countries, housing costs have skyrocketed, making ownership of a home impossible for many people. The increase in house prices has a significant impact on both the local economy and the overall well-being of residents. Investors who plan to use their home as an investment will be impacted by this issue in the long run (Jafari and Akhavian, 2019). As housing is important at all levels, so is the need for accurate prediction of house prices. This will provide confidence to policymakers as well as homeowners and investors.