SAS® Forecasting Project For Critical Thinking

SAS® Forecasting Project for Critical Thinking

Dataunit Typedr Dewey W Todd Dr Dewey W Toddh Housec Condo

Dataunit Typedr Dewey W Todd Dr Dewey W Toddh Housec Condo

Data Unit # Type Dr. Dewey W Todd: Dr. Dewey W Todd: H = House C = Condo/Apartment Location Dr. Dewey W Todd: Dr. Dewey W Todd: Mapping District U/S/R Dr.

Dewey W Todd: Dr. Dewey W Todd: U = Urban S = Suburban R = Rural Price Sq. Ft. Lot (Acres) Garage Dr. Dewey W Todd: Dr.

Dewey W Todd: Number of car parking spots provided (attached only) - garage or carport. BRs Baths Pool Dr. Dewey W Todd: Dr. Dewey W Todd: No = No Access to a pool HA = Access to shared pool (HOA) AG = Above Ground IG = In-Ground Age 1 H 10 R $ 54, No H 2 U $ 98,. No H 5 S $ 125,..5 AG H 6 S $ 250,.

No H 9 S $ 411,. No H 1 U $ 56,. No H 3 S $ 289,. HA H 7 R $ 420,.5 No H 4 S $ 199,. No C 8 U $ 249, HA H 10 R $ 77, No H 2 U $ 78,.

No H 5 S $ 199,. IG H 6 S $ 279,..5 HA H 9 S $ 842, HA H 1 U $ 66,..5 No H 3 S $ 311,. No H 7 R $ 311,. IG H 4 S $ 187,. IG C 8 U $ 311,.5 HA H 10 R $ 98, AG H 2 U $ 112,.

No H 5 S $ 146,. No H 6 S $ 301,..5 IG H 9 S $ 690,..5 HA H 1 U $ 71,..5 AG H 3 S $ 275,. No H 7 R $ 598, No H 4 S $ 176,. No C 8 U $ 405,.5 HA H 10 R $ 68,..5 No H 2 U $ 101,..5 IG H 5 S $ 117,..5 No H 6 S $ 266,.5 HA H 9 S $ 601,. IG H 1 U $ 39,..5 No H 3 S $ 401,..5 No H 7 R $ 782,.5 No H 4 S $ 201,.

HA C 8 U $ 199, HA H 10 R $ 119, No H 2 U $ 88,. AG H 5 S $ 188,. No H 6 S $ 231,. No H 9 S $ 485,..5 No H 1 U $ 48,. No H 3 S $ 366,.

No H 7 R $ 356, No H 4 S $ 157,. No C 8 U $ 288, HA H 10 R $ 49,. No H 2 U $ 91,..5 No H 5 S $ 179,. No H 6 S $ 189,..5 No H 9 S $ 532,..5 No H 1 U $ 52,. No H 3 S $ 399,..5 No H 7 R $ 388,.5 No H 4 S $ 175,..5 No C 8 U $ 301, HA H 10 R $ 95, IG H 2 U $ 96,..5 No H 5 S $ 171, No H 6 S $ 207,.

IG H 9 S $ 577,. HA H 1 U $ 49,. No H 3 S $ 247,. HA H 7 R $ 497,.5 IG H 4 S $ 205,..5 HA C 8 U $ 469,.5 HA H 10 R $ 77,. AG H 2 U $ 91,.

No H 5 S $ 152,. IG H 6 S $ 401, HA H 9 S $ 505,. IG H 1 U $ 58,. AG H 3 S $ 285,..5 IG H 7 R $ 675, IG H 4 S $ 188,. No C 8 U $ 302, HA H 10 R $ 171, AG H 2 U $ 84,.

No H 5 S $ 166,. No H 6 S $ 366,..5 HA H 9 S $ 411,. No H 1 U $ 68,. No H 3 S $ 297,..5 HA H 7 R $ 524, No H 4 S $ 181,. No C 8 U $ 312, HA H 10 R $ 88, No H 2 U $ 79,..5 No H 5 S $ 151,.

No H 6 S $ 302, No H 9 S $ 489,..5 No H 1 U $ 64,..5 No H 3 S $ 400,.5 HA H 7 R $ 711, No H 4 S $ 172,. IG C 8 U $ 345, HA H 10 R $ 81,.5 No 24 Name: _______________________________________________ SAS® Forecasting Project for Critical Thinking This project utilizes the “Real Estate – Base†database. The purpose is twofold: · Build critical thinking skills needed to structure data analysis appropriately for effective decision making. · Analyze available data practically and skillfully in order to build an explanatory regression model. The Real Estate - Base database includes the following variables for 101 homes ( NOTE: These variables are shown as qualitative variables within the database): a. Unit# (An assigned database key) b. Type (H = House, C = Condo/Apartment) c. Location (1 through 10 – voting district where located) d. *U/S/R (Urban vs. Suburban vs. Rural location) e. Price (The price the house ended up selling for in 2017) f. Sq. Ft. (Heated/Cooled & Attached square footage) g.

Lot (Acres) (Acreage of property) h. Garage (Number of attached covered and/or enclosed parking positions) i. BRs (Number of qualified bedrooms) j. Baths (Number of bathrooms – no tub or shower indicated as .5) k. *Pool (No=No Access; HA=Shared Pool; AG=Above Ground; IG=In Ground) l. Age (Age of home in rounded year at end of 2017) At a high level, here are the steps you are going to perform: A.

Download the Excel spreadsheet with the Real Estate Data in it and create the requested Scatterplots. NOTE: It is important that the Dependent Variable (Price) is on the Y-axis and the Independent Variable is on the X-axis. The order of the two columns will dictate that. B. Perform Regression Analysis within Excel to determine how well the prescribed Independent Variables explain changes in the Dependent Variable.

C. Upload the original Real Estate dataset into SAS Studio (before the changes in Step 2). D. Perform a series of Regression Analyses in SAS Studio to find a better set of explanatory variables. E.

Answer a critical thinking exercise regarding forecasting and the data set we have. Here are the steps in detail: 1. Create the following charts in Excel using the charting tools and the indicated variables in “Real Estate - Base.xlsx†(Remember, Price is your Dependent Variable) a. Create a new tab in the spreadsheet called “Scatterplotsâ€. After creating each Scatterplot on the original tab, move it to the Scatterplot tab you created.

Note that moving columns around may affect your already completed scatterplots! b. Create a Scatterplot using the variables Price and Sq. Ft. c. Create a Scatterplot using the variables Price and Lot (Acres). d. Create a Scatterplot using the variables Price and Garage. e.

Create a Scatterplot using the variables Price and BRs. f. Create a Scatterplot using the variables Price and Baths. g. Create a Scatterplot using the variables Price and Age. 2. What sort of relationship do you see between these variables based on the scatterplots? a.

Between Price and Sq. Ft. (Circle)? No relationship Weak Moderate Strong b.

Between Price and Lot (Circle)? No relationship Weak Moderate Strong c.

Between Price and Garage (Circle)? No relationship Weak Moderate Strong d.

Between Price and BRs (Circle)? No relationship Weak Moderate Strong e. Between Price and Baths (Circle)? No relationship Weak Moderate Strong f. Between Price and Age (Circle)? No relationship Weak Moderate Strong 3. In the Excel spreadsheet provided, using the Data Analysis Add-in, run a regression analysis with Price as the Dependent Variable and Lot, Garage and BRs as the Independent Variables and select to have Excel create a new tab called “Regression Modelâ€. It is recommended that you run individual regressions with each variable alone to see how strong each R2 is for Step 2.

4. Provide the following from the “Excel Modelâ€: a. Coefficient of Determination (R-squared) ___________________ b. Y-Intercept for the Regression Model ___________________ c. Slope value for X1 (Lot) ___________________ d. Slope value for X2 (Garage) ___________________ e. Slope value for X3 (BRs) ___________________

5. Do you think we need all three current Independent variables in our Regression model to predict changes in Price (Circle)? Yes No Explain: _________________________________________________________________________ _______________________________________________________________________________ _______________________________________________________________________________

6. If we don’t need all three, which variable(s) would you remove (Circle)?

Lot Size Garage BRs 7. Of the following variables in the spreadsheet, which variables would you select next to add to the model (i.e., you think it would create a stronger prediction of Price)? Type Location U/S/R Sq. Ft. Baths Pool Age 8.

Run a SAS Regression Model on the Real Estate – Base database using Price as the Dependent Variable (Y) and include the original Independent Variables (minus any you removed in step 6) and adding the variables you chose in step 7. Print your final model output and turn it in with the assignment. (NOTE: You may have to repeat this exercise until you find a combination of variables that gives you a higher R2). 9. Did your SAS model provide a stronger Coefficient of Determination (Circle)? Yes No Critical Thinking Question: 10.

A large real estate company is trying to build a model to forecast total sales for the coming year for each of their agents and they have pulled data from their Finance records. They are trying to assemble the best data to build a Regression model. a. Would it make sense for them to simply use the same database as we used above in the SAS model? Why or why not? __________________________________________________________________________________ __________________________________________________________________________________ b. Recommend three data elements you think they probably have available to help them predict sales for each of their sales people.

1. ______________________________________________ 2. ______________________________________________ 3. ______________________________________________

Paper For Above instruction

The development of robust regression models in real estate forecasting is a multifaceted process that involves meticulous data analysis and critical decision-making. This paper explores the process of building a predictive model for housing prices, emphasizing data visualization, statistical analysis, and strategic variable selection using tools like Excel and SAS Studio. The goal is to understand relationships between housing features and price, and to optimize the model for accuracy and predictive power.

Initially, the process begins with data visualization through scatterplots to visually assess the relationships between the dependent variable, price, and various independent variables such as square footage, lot size, garage spaces, bedrooms, baths, and age. Creating these plots in Excel involves setting price as the Y-axis and each independent variable as the X-axis, facilitating immediate visual insights into the strength and nature of relationships. For example, one might observe a generally positive correlation between price and square footage, indicating that larger homes tend to sell for higher prices. Conversely, age may show a weak or no apparent relationship, suggesting less influence on sale prices.

Following visual analysis, regression analysis in Excel quantifies these relationships. Running separate regressions with individual independent variables provides R-squared values that measure how much variance in price each variable explains independently. Variables such as lot size, garage capacity, and bedrooms may show varying levels of explanatory power. The coefficients from these regressions—including intercepts and slopes—are critical for understanding how each feature impacts price.

Deciding whether to include all variables in a multiple regression model involves assessing their individual significance and contribution to the model's overall performance. Variables showing weak relationships or low R-squared values may be candidates for removal. For instance, if bedroom count displays limited explanatory power, it can be excluded and replaced with other variables like property type, location, U/S/R (urban/suburban/rural), square footage, baths, pool presence, or age. Re-evaluating the model in SAS facilitates refining the set of variables for better predictive accuracy.

The iterative process continues with testing various variable combinations in SAS, seeking the highest R-squared value, which indicates a better-fitting model. Final models typically include the most influential variables, with coefficients guiding understanding of their effects. A higher R-squared in SAS compared to Excel indicates increased predictive strength, owing to multivariate analysis and model optimization.

In the context of predictive modeling for a large real estate company's sales forecasts, the relevance and quality of data are paramount. Using the same database employed for housing price prediction may not be optimal because different factors influence sales volume and revenue for agents. It is important for such organizations to incorporate additional or alternative data elements, such as historical sales data per agent, market trends, and client demographics. These elements can significantly enhance the accuracy of sales forecasts.

In conclusion, creating effective housing price models requires a systematic approach encompassing data visualization, regression analysis, and strategic variable selection. The use of Excel and SAS enables analysts to refine models continuously, improving their explanatory and predictive capabilities. For sales forecasting in broader real estate contexts, the integration of specialized data elements is critical to constructing accurate and actionable models, underscoring the importance of tailored data collection aligned with forecasting goals.

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