If you are buying or selling you need to know the price

From: Juan V Antunez, CCIM, Civil Engineer, Assistant Manager, Commercial Sales & Leasing, The Keyes Company 786-306-7647

If you are Buying or Selling you need; Useful Market Information plus Algorithms Plus Hedonic Regression Analysis plus Expertise to Maximize Your Wealth. Contact me because I can provide all the above to you. You can check my Experience and Expertise at www.juanantunez.keyes.com

Let’s say that you are trying to Sell;

Leave Money On The Table: One thing that you don’t want to do is to Leave Money On The Table when you sell. This can happen if you don’t know how to establish the asking price of your property based on what the competition is asking for their property. I have an algorithm that uses a Hedonic Regression Analysis at a 95% Confidence Level to Predict what should be the Asking Price of your property within a 95% Confidence Interval.

You need to Know What Is The Sale Price of Your Property: I have an algorithm that uses a Hedonic Regression Analysis at a 95% Confidence Level to Predict the Sale Price of your property within a 95% Confidence Interval.

Once you have this information then you use your judgment to establish the Asking Price and to negotiate the offers that you receive.

Below is a sample of a Hedonic Regression Analysis at a 95% Confidence Level to Predict the Sale Price of an Apartment Building in the Little Havana Area. Notice the small % difference between the Actual Sale Prices and the Predicted Sale Prices of the Sales used in the analysis.

ASKING   PRICE

ASKING GROSS INCOME MULTIPLIER

Upper range 95% Confidence Interval

Lower range 95% Confidence Interval

Predicted Sale Price

1,554,992

$1,533,948

$1,112,193

$1,323,071

Predicted gross income multiplier

Actual gross income

95% confidence interval

95% confidence interval

0

Predicted gross income

Average monthly income per unit

Average monthly income per unit

Effective Year Built

ADDRESS

Sale Price

Predicted Sale Price

% difference Sale Price vs Predicted Sale Price

2013

1023 SW 6 ST

10700000

10707511

-0.07%

2008

1529 NW 1 ST

1175000

1191751

-1.43%

2008

1135 NW 4 ST

1440000

1456854

-1.17%

2007

1876 SW 11 TER built 1925 completely remodeled 2007

2266002

2296200

-1.33%

2000

1132 NW 3 ST

950000

1105704

-16.39%

2000

1609 SW 14 St remodeled change year from 1924 to 2000

775000

836834

-7.98%

2000

1330 NW 5 ST

535000

449155

16.05%

1997

534 NW 11th Ave needed repairs

670000

681267

-1.68%

1996

1468 SW 3 ST

619000

484065

21.80%

1987

217 SW 15 AV # 4

430000

348671

18.91%

1981

949 NW 5 St

650000

778341

-19.74%

1974

932 NW 5 ST

785000

773953

1.41%

1974

528 NW 11th Ave

670000

641051

4.32%

 

The F Statistic, or the F-observed value. Use the F statistics to determine whether the observed relationship between the dependent and independent variable occurs by chance. Here F=

 

1857.722581

 

Significance F. This indicates the probability that the Regression output could have been obtained by chance. A small Significance F confirms the validity of the Regression output. For example, if Significance of F = 0.030, there is only a 3% chance that the Regression output was merely a chance occurrence.

Significance F

 

Here the Significance F is a miniscule number

2.58341E-57

 

If F is > Significance F

 

The elements of comparison are significant

 

Here F/Significance F =

 

This is an astronomical number

7.19097E+59

 

The elements of comparison are Extremely significant

 

 

 

 

 

 

 

 

SUMMARY OUTPUT

 

Regression Statistics

The "Multiple R' measures the strength of the association; it doesn't reflect the direction

0.997212572

Multiple R

0.997212572

The 'R Square' value is, as the name implies, the square of the correlation coefficient for the regression.  This measures the strength of the regression prediction compared with predicting solely by the response mean.  The important thing is: The closer to 1 the stronger the regression prediction, the closer to 0 the weaker.  [To be precise: (r2) % of the observed variability in Y can be attributed to X

0.994432914

R Square

0.994432914

Adjusted R Square

0.993897617

Standard Error

107590.6059

 

Observations

58

 

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