You work for a real estate company in Seattle, WA. Part of your business is to buy undervalued properties in the market, renovate them, and resell later at higher prices. Your job is to evaluate what properties on the market are worthy of buying. Accounting for all incomes, such as commissions and reselling markups, and costs, such as inspection, renovation, property management, etc., it is profitable for your company to buy a property if its predicted transaction price is at least 12% higher than its asked price.

Worksheet "DataDescription" provides the description for the variables of the dataset.

Use this link for data: https://docs.google.com/spreadsheets/d/1aHq7ezoqxEQjLPyBbUvCNXfRpWNbsOCE/edit?rtpof=true

(1) Worksheet "2014Transactions" records the historical property transaction data of some 118 properties from May to December 2014 in Bellevue, WA, 98005. Variable "Transaction_Price" records the transaction price for each transaction.

Use linear regression to build a predictive model for property transaction prices in Bellevue. Report the formulae for your predictive model. (Hint: use transaction price as the dependent variable, and other relevant variables as the independent variables)

(2) You will use your predictive model to decide whether to buy some of the available properties on the market in that area. Worksheet "PropertiesOnMarket" records the information for all the 25 available properties. Variable "Asked_price" records the asked prices for the properties.

You will advise your company to buy a property if its predicted transaction price is at least 12% higher than its asked price.

Which properties in these 25 properties you will recommend your company to buy? (Hint: use your predictive model from question (1) to predict the transaction prices for these 25 properties, then compare them with their asked prices)