Test 1 Comparable Sales Analysis

Real Estate Investing Success

Exposing The True Secrets Of Real Estate Investing

Get Instant Access

The first section of the model allows the user to enter information for comparable home sales. This information is needed to help make accurate projections of the estimated resale value of an investment property and can be easily obtained by almost any local real estate sale agent. In fact, if you ask them, real estate agents can send the comps online to your e-mail address, which you can then access via the Internet. Next in this section is a provision that allows users to make adjustments to the sales price of the comps. This section provides users with the ability to compare properties on an apples-to-apples basis, just as an appraiser would do. For example, if the subject property has a central air-conditioning system and the comparable sale property does not, the price of the comparable sale will need to be revised upward in the adjustments to price section. This is exactly how real estate agents and appraisers derive the market value of a house. They start with an average price per square foot of several similar houses that have recently sold and make adjustments to compensate for differences in value.

The comp averages section simply takes an average of the three comps' sales prices to come up with an average sales price. This number is then divided by the average price per square foot. The result is a weighted average price per square foot. There is also a provision that allows users to turn the comps section off or on. As users become more and more familiar with a specific market or neighborhood, they are likely to already know what the average sales price per square foot is, so the sales data really doesn't need to be keyed in. Instead, the comps section can be turned off and the user's own estimate entered in.

Table 10.1 Property Analysis Worksheet. The Value Play Rental House Analyzer

Propety Value Analysis

Comp #1

Address: 123 S. Sample Drive ! Sales Price 365,500.00 ■ Adjustments to Price 1.600.00 Adjusted Price 367,100.00 Square Feet 2.645.00 Price Per Square Foot ____1?8-79

Comp #2

Address:

456 N. Sample Drive

| Sales Price Adjustments to Price i Adjusted Price I Square Footage ! Price Per Jiquarepoot^

Comp #3

Address: Sales Price Adjustments to Price Adjusted Price Square Feet Price Per Square Foot

789 S. Sample Drive

294,000.00 3.200.00 297,200.00 2.200.00 135.09

Comp Averages

Subject Property

Property

Adjustment to Comps

2.50

421 Moroni Blvd.

Values

2.50 | 0.00

(2.50)

Sales Price

316,466.67

Purchase Price

370,000

Fair Market Value

390,956 383,956

376,956

Adjustments to Price Adjusted Price

800.00

Square Feet

2.800.00

Actual Price

370.000 370.000

370.000

317,266.67

Price/Sq Ft

132.14

Difference

20,956 13,956

6,956

Square Feet Price Per Square Foot

2.313.67 137.13

Est Price/Sq Ft If Turned OFF

ON I 135.00 ________j__

Financing and Income Analysis

Cost and Revenue Assumptions

Financing Assumptions

Key Rent Ratios

Purchase Price

370,000

Total Purchase

100.00%

373,500

Total Square Feet

2,800.00

Improvements

0

Owner's Equity

10.00%

37.350

Total Price/Sq Ft

133.39

Closing Costs

3,500

Balance to Fine

90.00%

336,150

Fair Market Value/Sq Ft

137.13

Total

373,500

Rental Income/Sq Ft

0.63

Annual

Monthly

Total Income/Sq Ft

0.71

Estimated Monthly Rent Income

1,750

Interest Rate

6.000%

0.500%

Capitalization Rate

4.10%

Other Income

250

Amort Period

30

360

Gross Rent Multiplier

17.79

Total Income I

2,000

Payment

24,185

2,015

Operating Efficiency Ratio

2.72

Rental Increase Projections Average Monthly Rent Operating Expense Projections

0.00%

4.00%

3.50% I

3.50% !

3.50%

i 1,750

1,820

1,884

1,950

2,018

: 0.00%

2.00%

2.00%

1.50%j

1.50%

Actual

Projected

Operating Revenues

Monthly

Year 1 |

Year 2 |

Year 3 |

Year 4

Year 5

Gross Scheduled Rental Income

1,750

21,000

21,840

22,604

23,396

24,214

Vacancy Rate

5.0%

88

1.050

1.092

1.130

1.170

1.211

Net Rental Income

1,663

19,950

20,748

21,474

22,226

23,004

Other Income

250

3.000

3.120

3.229

3.342

3.459

Gross Income

100.0%

1,913

22,950

23,868

24,703

25,568

26,463

Operating Expenses

Repairs and Maintenance

5.2%

100

1,200

1,224

1,248

1,267

1,286

Property Management Fees

5.5%

105

1,260

1,285

1,311

1,331

1,351

Taxes

18.3%

350

4,200

4,284

4,370

4,435

4,502

Insurance

2.9%

55

660

673

687

697

707

Salaries and Wages

0.0%

0

0

0

0

0

0

Utilities

0.0%

0

0

0

0

0

0

Professional Fees

1.3%

25

300

306

312

317

322

Advertising

0.0%

0

0

0

0

0

0

Other

0.0%

0

0

0

0

0

0

Other

0.0%

0

0

0

0

0

0

Other

0.0%

0

0

0

0

0

0

Total Operating Expenses

33.2%

635

7,620

7,772

7,928

8,047

8,167

Net Operating Income

66.8%

1,278

15,330

16,096

16,776

17,521

18,295

Cash Flow From Operations

Total Cash Available for Loan Servicing

1,278

15,330

16,096

16,776

17,521

18,295

Debt Service

2.015

24.185

24.185

24.185

24.185

24,185

Remaining CF From Ops

(738)

(8,855)

(8,089)

(7,409)

(6,663)

(5,889)

Plus Principal Reduction

335

4.128

4.383

4.653

4.940

5.245

Total Return

(403)

(4,727)

(3,707)

(2,756)

(1,724)

(645)

CF/Debt Servicing Ratio

63.39%

63.39% i

66.55%

69.36%

72.45%

75.65% 1

Net Operating Income ROI

]

41.04%:

43.09%

44.91%

46.91%

48.98%

Cash ROI

-23.71%;

-21.66%

-19.84%

-17.84%¡

-15.77%

Total ROI

-12.66%;

-9.92%

-7.38%

-4.61%

-1.73%

In this example, three comparable sales of properties similar in characteristics and close in size to the subject property were used. The objective was to use houses in desirable locations also having lake views. In the subject property section, simply enter in the purchase price of the house being analyzed. In this example, the original asking price by the seller was $370,000. The asking price is a good place to begin the analysis, but users can also easily experiment with the model by merely changing the asking price, which is exactly what we are going to do in Table 10.2. After entering the purchase price, the square footage of the subject property is entered, which in this example is 2,800. The average sales price per square foot from the comps section is then fed into the subject property section. The purchase price per square foot is automatically calculated and then multiplied by the square footage of the subject property under the property values and adjustment to comps sections.

The adjustment to comps cell is used to create an estimated fair market value (FMV) using a range of property values for three different scenarios—best case, most likely, and worst case. In this case study, $2.50 per square foot is used; however, the number can be changed to anything you want it to be. For the best-case estimated fair market value, the model adds $2.50 to the price per square foot cell in the comp averages section, and then multiplies the sum of the two by the square feet of the subject property. The positive value of $2.50 is visible under the adjustment to comps heading. Here's how the calculation works:

Best-Case Estimate of Fair Market Value

(Average price per square foot + adjustment to comps) x subject property square feet = best-case estimate of FMV ($137.127 + $2.50) x 2,800 = $390,956

The most likely estimate of the FMV calculation in the model neither adds nor subtracts the value of $2.50 to the price per square foot cell in the comp averages section. As you can see in Table 10.1, it is set to zero. This calculation is merely the product of the average price per square foot and the number of square feet. Take a moment to examine the calculation.

Most Likely Estimate of Fair Market Value

Average price per square foot x subject property square feet = most likely estimate of FMV $137.127 x 2,800 = $383,956

For the worst-case estimate of FMV, the model subtracts $2.50 from the price per square foot cell in the comp averages section, and then multiplies the difference of the two by the number of square feet of the subject property. Take a minute to review the calculations.

Worst-Case Estimate of Fair Market Value (Average price per square foot - adjustment to comps) x subject property square feet = worst-case estimate of FMV ($137.127 - $2.50) x 2,800 = $376,956

The purpose of creating three different scenarios in the model is to provide a range of estimated fair market values. This allows the user to evaluate the very minimum FMV that might be expected on the low end of the price range, and the very highest FMV that might be expected on the high end of the price range. In this example, using $2.50 provides a total range of $5.00 per square foot—from +$2.50 to -$2.50.

Now let's take a moment to interpret the output. What is the model telling us? The three different values provide us with a minimum estimate for FMV and a range of values up to a maximum estimate for FMV. In order for the subject property to pass the first of the two tests, which is the comparable sales test, it must meet a minimum threshold of any value greater than zero by taking the difference of the FMV and the actual purchase price. In this example, the subject property passes the test under all three price scenarios reflected in the model, with positive values of $20,956 for the best-case estimate, $13,956 for the most likely estimate, and $6,956 for the worst-case estimate. If everything else looks good in the rest of the model, it is acceptable to have a negative value under the worst-case estimate. The most likely and best-case estimates, however, must have positive values. The difference in values shown in each of these three scenarios represents a discount of the actual purchase price to the market. So far, so good. Our subject property has passed the first test.

Was this article helpful?

0 0
What You Need to Know About Real Estate

What You Need to Know About Real Estate

Ready to Find Your Dream Home? Don’t Let the Search Turn into a Nightmare Discover the Tips, Tricks, Techniques & Secrets You Need to Know to Turn Your Dream of Owning a Home into Reality!

Get My Free Ebook


Post a comment