The stock market is not the only finance market one should seek to understand. Real estate is another one that for many is closer to their daily lives. We all need a place to live, and whether to rent or buy is a decision that keeps popping up when one progresses through stages of life.
In Module 5 we explored the correlation of two time series (two different stock prices or the autocorrelation of a time series to a shifted version of itself) and how we will dig deeped into this concept and take it one step further by examining a linear regression model that emerges (largely as a happy side product) from the calculations we do to detemine the correlation.
We also need to learn to be creative with our data sources. Assume we want to establish a relationship between what a residential property costs (if one should buy the property) versus what the monthly rent of that same property would be. Although some cross-list their real estate as "buy or rent", most properties are listed as either-or. Unless we are able to create a pair dataset of "sales price" versus "rent price", we cannot check for a correlation and — alas — cannot fit the regression model either.
One way to go about this dilemma is to seek out listings for similar properties (near one another, about the same size, roughly the same year of construction, etc.) and then "bucket" two or more listings into one, using the average sales price of the bucket together with the averare rent amount of the bucket to create a single data point for our analytics purposes. This module is an exercise in creativity to seek out data, compare it, group it, represent it and then build a model for it. Once the model is built, we can begin to pose questions such as "if a house costs this much, then what would it cost to rent one like it?". With some basic math, we can use the same model to pose a vice-versa question: "if this is how much rent is charged for an apartment like this, what would it cost to buy one?".
This module will help you do the following:
An online guide is the pre-class reading for this module.
Financial decision making is not the easiest. Charging too much might make it hard to find a lessee while charging too little, although philantrophic, is bad for business. To figure out what's a good price, one has to get a feel for the market. In the case of setting a rent, one may wish to be inspired by rents of similar residences in similar locations, while staying safely away from illegal rent fixing. Elaborate in writing a paragraph of two what your gut feeling is on what data one should gather before setting the amount and how would one go about adjusting it to observed demand?
After this module, you should be familiar with the following concepts:
Remember that you can always look concepts up in the glossary. Should anything be missing or insufficient, please report it.