# SPSS Homework Help

The following analysis is based on the statistical and computer valuation applications. Valuation modelling case study is also included in the SPSS assignment help solution. Students who wish to seek SPSS homework help should find this informative as well as useful. The solution also has the brief summary reports on Market identification, Valuation analysis, Data exploration outcomes etc. ANOVA and regression analysis were used to evaluate and interpret the outcomes.

University Gate is hypothetical 32 unit low-rise strata residential project located in a mixed use suburban neighborhood in a growing market. The Target market will  be professionals seeking to downsize, early retired empty nesters, or other higher net wealth individuals seeking a higher quality second home and/or investment opportunities in the area. The projects quality of SPSS project help construction and finish will be “Above Average” in relation to competition in the given market segment. It is located near community shopping and a park giving it a SPSS assignment solution location quality ranking of “Average”. The suite type mix for the proposed residential complex is as under:

 Suite Type No. of Units 1 BDRM + DEN 8 2 BDRM 12 2 BDRM + DEN 12 Total 32

Note: Refer Appendix -1 for more Suite Type details

Executive Summary

61% of data analyzed belongs to the statistics using SPSS assignment help new construction, which became basis for valuation of proposedSPSS project help project in this assignment. Most popular suite type in market area was 2 bedrooms, 2 bathrooms with average area around 900 Sifts. Common facilities such as Secure Parking, Share Car, Parking Spaces are nor necessary to sell property and no longer a differentiator. Only Quality, Location, Floor and Bedrooms is important variables to define sales price.

Market Identification process has been identified for this assignment along the below mentioned lines:

• Problem to be solved

Choosing correct market segment is SPSS assignment solution vital in success of any property appraisal and associated analysis. In this process first problem is identified and its scope is defined so that problem can be solved. In this assignment, problem has been SPSS project help defined as determining the pricing of each strata unit after considering competitive market, buyer sentiment and industry trends. These will be based on client feedback.

• Scope

In this assignment market segment scope has been defined as target client comprising of professionals, retired people, or other SPSS assignment help higher net wealth individuals seeking a higher quality second home and/or investment opportunities in the area. Scope of work also involves applying thehelp with SPSS assignment statistical methods and using a supplied database of comparable sales (condo sales) to come up with a valuation model that will be successful. Here we as appraiser need to identify the solution data analysis using SPSS assignment help strategy after consulting with prospective clients.

• Similarity

Using a database of comparable sales (condo sales), we need to come up with the economical and statistically similar units in the proposed project, which may be marked to the market identically to that given in the database to arrive at pricing benchmarks. For this we consider SPSS homework help four dimensions of the similarity viz. transaction, time, space and utility. These will be analyzed to see how they affect our reduction of database into sets of statistics using SPSS assignment help data information useful for analyzing and understanding market dynamics by applying them to our data decisions. This will be a  measure of relative equivalence in aspects of time, space and utility (physical, legal and financial qualities).

• Data decisions

Decision about sales price and valuation of strata units has to be data driven and hence market driven with economical, statistical andhelp with SPSS assignment probabilistic view of the market segments. This will involve statistical, economical and market analysis and research. Truecustomers PSS problems with answers requirement of market segment must be uncover and proposed offerings should align to it using data analytics. This will involve mainly make my SPSS homework defining and refining (reducing) available data.

• Optimality

Main approach that seems prudent in this assignment is to first identify the right market segment or do market identification SPSS questions with answers and then locate the subject within that market or do property characterization. Here first we examine the effect of the client’s appraisal problems and then determine the scope of the solution.After similarity analysis and data analysis using SPSS assignment help decisions, the issue of optimal use SPSS problems with answers will be included in the same sequence to understand the initial perception of the right market and right data sets. Research and analysis may lead to a reconsideration of each of the above steps iteratively.

To sum up, we say that market identification step identify the clients and other intended users, Intended use of property, purpose, date of value, property characteristics and appraisal assumptions. These will ensure understanding data analysis using who is the client, problem scenario,statistics using SPSS assignment help what data is and what variables are important; identify the time dimension of the valuation solution, and hence to identify SPSS homework help the correct statistical tools to be used for analysis.

Valuation Problem Checklist framework has been provided in Appedix-2, which is a basis for preliminary analysis and market SPSS questions with answers identification in this assignment.

Data Exploration Outcomes

• Database overview

Database we are using contains following fields:

New Construction (Y/N),

Unit Number,

Street Number,

Street Name,

Building Name,

Year Built,

Floor,

Orientation,

Bed rooms,

Bathrooms,

Parking Spaces,

Secure Parking,

Size,

Sale Date,

Sale Price,

Sale Price per Sqft,

Sale Price per Bed room,

Abutting influence,

Competitive Set Quality,

Competitive Set Rank,

Car Sharing,

Location Description,

Location Quality,

Location Quality Rank

Dataset contains details of 76 properties.

Variable of interest are classified as under:

 Grouping Variables Variables candidate for recoding Dependent Variables (DV) Independent Variables (IV) New Construction (Y/N), Street Name, Building Name, Orientation, Secure Parking, Abutting influence, Competitive Set Quality, Car Sharing, Location Description, Location Quality, Suite Type New Construction (Y/N) Secure Parking (Y/N) Bedrooms Bathrooms Car sharing (Y/N) Size, Sale Price, Sale Price per Sqft, Sale Price per Bed room Unit Number, Street Number, Year Built, Floor, Bed rooms, Bathrooms, Parking Spaces, Sale Date, Competitive Set Rank, Location Quality Rank, New Const Recoded, Secure Park Recoded, Car Share Recoded, DD, MM, YYYY

Data revelation

It has been observed that dataset contains some missing information. For example, field New Construction have missing data  at record number 12, 13, 16, 26, 45, 47 and 64. All of these missing data we filled using clue from Year Built field. Year Built 2008 is considered new construction and year 1999 is considered old construction.

Other missing informationdata analysis using SPSS homework help includes a Year Built field building CROSSROADS, floor information missing for building Maple Village. There were many missing fields information in field Abutting Influence and a missing information for field Car sharing was missing for property PEARL. This was changed by us as PEARL has two records that completed the information. Most of these fields we decided to keep blank as no surety on correct information based on available data.

• Data re-expression

Variables New Construction (Y/N), Secure Parking (Y/N) and Car sharing (Y/N) has been recoded as Binary variables as Newstatistics using Const Recoded, Secure Park Recoded and Car Share Recoded respectively using following code:

RECODE NewConstruction (‘Y’=1) (‘N’=0) INTO NewConst_Recoded.

VARIABLE LABELS  NewConst_Recoded ‘New Const Recoded’.

EXECUTE.

RECODE SecureParking (‘Y’=1) (‘N’=0) INTO SecurePark_Recoded.

VARIABLE LABELS  SecurePark_Recoded ‘Secure Park Recoded’.

EXECUTE.

RECODE CarSharing(‘Y’=1) (‘N’=0) INTO CarShare_Recoded.

VARIABLE LABELS  CarShare_Recoded ‘Car Share Recoded’.

EXECUTE.

Above recoded variables will be used as IVs.

New grouping variable Suite Type has SPSS homework solution been created using variables Bedrooms and Bathrooms as grouping variable as follows:

COMPUTE SuiteType=concat (string (Bedrooms, F4.1), ” BDRMS +” ,string (Bathrooms,F4.1), ” BATHS”).

VARIABLE LABELS  SuiteType ‘Suite Type’.

EXECUTE.

We also created three new  variables, DD, MM, YYYY to capture Sale date in numeric (integer) format for using them as IV in our analysis. Transformation codes used are as under:

COMPUTE DD=Xdate.Mday (SaleDate).

VARIABLE LABELS DD ‘DAY’.

EXECUTE.

COMPUTE MM=Xdate.MONTH (SaleDate).

VARIABLE LABELS  MM ‘MONTH’.

EXECUTE.

COMPUTE YYYY=Xdate.Year (SaleDate).

VARIABLE LABELS YYYY ‘YEAR’.

EXECUTE.

• Discussion of the data to be used in valuation step

New Construction and Year Built grouping variables have been statistics using  explored in the form of frequency distribution (Refer Appendix – 3). Since 61% of new construction happened in year 2008, it has been decided to use only data where Year Built was 2008 in valuation step. Hence only 46 records out of 76 records SPSS homework help will be used in valuation step. This is first data reduction opportunity in this assignment to ensure statistics using that valuation is based on most recent available information.

Correlation analysis has been performed between SPSS homework solution all the Independent variable and two IV variables having correlation above 0.70 has been identified as opportunity for data reduction. Similarly correlationSPSS homework help analysis of all the DVs has been performed and DV Sales Price per Bed room has been eliminated (Refer Appendix – 7). Using reduced set of IVs and DVs correlation analysis is again performed and final set of DV and IVs have arrived at for data analysis using SPSS homework help valuation step (Refer Appendix-8). Finally following are the reduced set of variables for valuation stage:

 Grouping Variables Dependent Variables (DV) Independent Variables (IV) Orientation, Floor, Bed rooms Secure Parking, Abutting influence, Competitive Set Quality, Car Sharing, Location Quality, Suite Type Size, Sale Price, Sale Price per Sqft Street Number, Floor, Bed rooms, Competitive Set Rank, Location Quality Rank

Relationships between selected DV and IV have been linear as confirmed by scatter plots shown in Appendix – 9.

Valuation Analysis

• Discussion of methods available

The method we propose toSPSS homework solution use for estimating selling price of new condos is Statistical Analysis of Sales history at similar unit level. Measures of central tendency such as Mean and Median have been used as a test and cross-check for market value estimates. Statistical analysis is also used to identify a SPSS homework help probable value range. Advantage of this approach is this that without much effort, we could find the probable value range of different units of new property suites. Main disadvantage of this approach seems prerequisite to ensure that market properties with which we are comparing are similar in all or most aspects. There can be requirement for valuing the property as at time value of time of sales. This means that we need to have some estimate of growth rate that may data analysis using SPSS homework help be applied as adjustment to arrive the correct valuation of our property as at the time of sale. Other problem could be this that we help with SPSS homework do not find exact market comparable of what is proposed to be offered for sale in terms of area and suite types. This means that market pay for SPSS homework differentiators might not be exact but gross. For e.g. 1 BDR, 2 BRD might be better SPSS homework solution comparable then 1BRD + 2 Baths, etc.

• Decision on unit of comparison
 Unit Suite Type Mean Median SD Min Max Range 95% CI Lower 95% CI Upper Size 1.0 BDR 625.90 615.00 51.997 550 690 140 588.70 663.10 1.5 BDR 828.80 856.00 56.389 745 887 142 758.78 898.82 2.0 BDR 990.69 996.00 133.805 800 1411 611 939.79 1041.59 2.5 BDR — — — — — — — — Sales Price 1.0 BDR 291.35 260.66 59.825 234.00 378.25 144.25 248.55 378.25 (‘000) 1.5 BDR 350.53 394.30 95.628 246.00 443.10 197.10 231.79 469.26 2.0 BDR 398.97 393.98 83.912 267.80 611.10 343.30 367.05 430.89 2.5 BDR 474.48 474.48 25.067 456.75 492.90 35.45 249.26 699.69 Sales Price per SqFt 1.0 BDR 461.96 439.85 61.139 401.02 557.52 156.50 418.22 505.69 1.5 BDR 418.97 444.53 93.773 307.50 517.64 210.14 302.53 535.40 2.0 BDR 403.57 396.54 70.504 250.00 542.72 292.72 376.75 430.38 2.5 BDR 406.23 406.23 21.461 381.05 421.40 30.35 213.41 599.04

Since average area of the market units do not match with the area of the proposed New Condos in One Bedrooms and Two Bedroom suites (Refer Appendix-1 for subject units), it seems prudent to select help with SPSS homework measure of unit as Sales Price per SqFt.

As can be seen from the correlation analysis given in Appendix – 8, the key explanatory variables without requirement pay for SPSS assignment for any adjustments are as under:

Street Number, Floors, Bedrooms, Competitive set rank and location quality rank

• Table of prices
 Suite# Floor Orientation Description Bedrooms Baths Size (SqFt) Sales Price (\$ ‘000) 101 1 SOUTH 2 BDRM 2 2 920.00 364.82 102 1 SOUTH 1 BDRM + DEN 1.5 1.5 775.00 340.88 103 1 NORTH 2 BDRM 2 2 940.00 372.75 104 1 NORTH 2 BDRM + DEN 2.5 2 975.00 386.63 105 1 NORTH 2 BDRM + DEN 2.5 2 1020.00 404.47 106 1 NORTH 1 BDRM + DEN 1.5 1.5 860.00 378.27 107 1 SOUTH 2 BDRM 2 2 840.00 333.09 108 1 WEST 2 BDRM + DEN 2.5 2 995.00 394.56 201 2 SOUTH 2 BDRM 2 2 920.00 364.82 202 2 SOUTH 1 BDRM + DEN 1.5 1.5 775.00 340.88 203 2 NORTH 2 BDRM 2 2 940.00 372.75 204 2 NORTH 2 BDRM + DEN 2.5 2 975.00 386.63 205 2 NORTH 2 BDRM + DEN 2.5 2 1020.00 404.47 206 2 NORTH 1 BDRM + DEN 1.5 1.5 860.00 378.27 207 2 SOUTH 2 BDRM 2 2 840.00 333.09 208 2 WEST 2 BDRM + DEN 2.5 2 995.00 394.56 301 3 SOUTH 2 BDRM 2 2 920.00 364.82 302 3 SOUTH 1 BDRM + DEN 1.5 1.5 775.00 340.88 303 3 NORTH 2 BDRM 2 2 940.00 372.75 304 3 NORTH 2 BDRM + DEN 2.5 2 975.00 386.63 305 3 NORTH 2 BDRM + DEN 2.5 2 1020.00 404.47 306 3 NORTH 1 BDRM + DEN 1.5 1.5 860.00 378.27 307 3 SOUTH 2 BDRM 2 2 840.00 333.09 308 3 WEST 2 BDRM + DEN 2.5 2 995.00 394.56 401 4 SOUTH 2 BDRM 2 2 920.00 364.82 402 4 SOUTH 1 BDRM + DEN 1.5 1.5 775.00 340.88 403 4 NORTH 2 BDRM 2 2 940.00 372.75 404 4 NORTH 2 BDRM + DEN 2.5 2 975.00 386.63 405 4 NORTH 2 BDRM + DEN 2.5 2 1020.00 404.47 406 4 NORTH 1 BDRM + DEN 1.5 1.5 860.00 378.27 407 4 SOUTH 2 BDRM 2 2 840.00 333.09 408 4 WEST 2 BDRM + DEN 2.5 2 995.00 394.56

Median Sales Price per SqFt has been applied to the area of the New Condo to determine the Pre-Sales prices. These pricesSPSS homework for money are without regard to the relevant data such as Floors, Streets; Competitive set rank and location quality rank. We will do these adjustments after comparing these prices with the one determined by using multiple regression advanced statistical analysis technique.

• Optional: regression analysis and value estimates
 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson 1 .911a .829 .807 31.99972 1.444 a. Predictors: (Constant), Location Quality Rank, Bedrooms, Floor, Street Number, Competitive Set Rank b. Dependent Variable: Sale Price per Square Foot
 ANOVAa Model Sum of Squares Df Mean Square F Sig. 1 Regression 194005.009 5 38801.002 37.892 .000b Residual 39935.301 39 1023.982 Total 233940.310 44 a. Dependent Variable: Sale Price per Square Foot b. Predictors: (Constant), Location Quality Rank, Bedrooms, Floor, Street Number, Competitive Set Rank Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. 95.0% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 154.367 38.234 4.037 .000 77.031 231.704 Street Number .029 .014 .183 2.048 .047 .000 .059 Floor 23.604 5.471 .306 4.315 .000 12.538 34.669 Bedrooms -73.347 11.515 -.450 -6.369 .000 -96.639 -50.054 Competitive Set Rank 28.702 11.278 .239 2.545 .015 5.891 51.514 Location Quality Rank 67.161 17.063 .404 3.936 .000 32.647 101.675 a. Dependent Variable: Sale Price per Square Foot

Model Equation:

Sales price per SqFt = 154.367 + 0.029 * Street Number + 23.604 * Floor – 73.347*Bedrooms + 28.702 * Competitive set rank + 67.161*Location Quality Rank

Recommendations:

• Strengths and weakness of various methods

We used median value of market data based on #Bedroom to arrive at Pre-Sales Sales Price which was then adjusted by more refined multiple regression model. Although mean sales price of our proposed project is found to be same at \$371, there are variations in sales price ranging statistics using SPSS homework help from -12% to 14%. Variations decreased as we go up the floor. Standard deviation of these variation in price remained between 3.7% to 4.9% floor wise, but increased up to 7.3% overall. Hence this model is good starting model, but needs refinement and may not be generalizable to help with SPSS homework all real estate projects as of now.

Method used had limitation of not SPSS assignment for money having comparable data in terms of Street Number, Competitive set rank and Location Quality Rank, which is assumed based on information given in the case and may not be right thing to do. Strength of regression model help with SPSS homework is this that all the relevant and significant variables got counted when sales price was estimated using this model.