naa maximize 2015 - presentation on in-depth analytics of pricing discovery

#MAMConf15

In-depth Analytics of Pricing Discovery

Donald Davidoff, D2 Demand Solutions

Annie Laurie McCulloh, Rainmaker LRO

Rich Hughes, RealPage

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Agenda

1. Forecasting • Forecasting Model Options • Principles of Forecasting • Forecasting Methods • Time Series Models • Forecast Accuracy

2. Assessing Amenity Values 3. Procedurally Generated Content 4. Analyzing Performance

• Methodology

• Revenue Performance

• Intangible Benefits

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Forecast Model Options and Design

Theoretical Probability: Coin: P(heads) = 1 head on a 2 sided coin = 1 out of 2

= 1

2

Dice: P(6) = 1 side out of 6 sides of a die (1,2,3,4,5,6) = 1 out of 6

= 1

6

Both Heads and a 6 together: = P(heads) * P(6)

= 1

2 *

1

6

= 1

12 or 8.3%

Experimental Probability: Identify a trial: • One trial consists of flipping a coin once and

rolling a die once • Conduct 25 trials and record your data in the

table below:

Question: You are handed one die and one quarter. What’s the probability of rolling a 6 and getting a heads at the same time?

Legend: Coin: H = Heads, T = Tails Die: 1,2,3,4,5,6 = number rolled on the die Head & 6: Y : Heads & 6 occurred, N: All other results Results: 1 trial out of 25 resulted in a heads and a 6 = 1/25 Therefore, P(heads,6) = 4%

1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy

Trial 1 2 3 4 5 6 7 8 9 10 11 12 13

Coin T T T H T T T T H T T T T

Die 4 1 1 6 2 5 5 6 5 1 1 5 6

Head & 6 N N N Y N N N N N N N N N

Trial 14 15 16 17 18 19 20 21 22 23 24 25

Coin H H H H T H H T T H H H

Die 2 1 2 1 5 1 2 3 2 1 4 2

Head & 6 N N N N N N N N N N N N

Results

Results

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Principles of Forecasting 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy

Grouping of Data

Forecast Accuracy

Quantity of Data

Forecast Accuracy

Recent Data

Forecast Accuracy

• Forecasts contain risk and uncertainty - they are rarely perfect

• Some characteristics of the data used to forecast can improve accuracy

• Forecasts should be systematically evaluated over time for accuracy

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Principle of Aggregating Data

• Since many times we must forecast off of sparse data, what are

some of the ways we aggregate data in our revenue management

forecasts?

- Lease type – Conventional New & Renewal, Affordable, Student, etc.

- Lead Source – ILS Vendor, Craig’s List, Property Website, Outdoor, etc.

- Unit types

- Lease terms

- Week types

- Move-in weeks

- Clustered communities

- Market

• Need “enough” observations/transactions to have predictive

capabilities


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Forecasting Methods

• Qualitative Methods

- Educated guesses based

on human judgement and

opinion

- Subjective and non-

mathematical

Executive Opinion

Market Research

Delphi Method

• Quantitative Methods

- Based on mathematics

- Consistent and objective

- Only as good as the data

on which they are based

Time Series Models

Causal Models

Associative Models


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Time Series Model

• Many of the forecasts used in revenue management

leverage time series models

• Time series models use historical data as the basis for

estimating future outcomes

- Moving average

- Weighted moving average

- Kalman filtering

- Exponential smoothing

- Autoregressive moving average (ARMA)

- Autoregressive integrated moving average (ARIMA)

- Extrapolation

- Linear prediction

- Trend estimation

- Growth curve


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Time Series Examples

Uniform distribution between 1 and 2

Increasing trend

Quadratic growth trend

Seasonal Model


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Time Series Problem - Seasonality

• A community manager must develop forecasts for the next

year’s quarterly or seasonal leads.

• The community has collected quarterly lead data for the

past two years.

• She has forecast total leads for next year to be 9000.

• What is the forecast for each quarter or season of next

year?


#MAMConf15 Time Series Problem

2-period Moving Average

Quarter 2014 ‘14 Index 2015 ’15 Index Avg. Index

2016

Fall 1900 ? 1900 ? ? ?

Winter 1400 ? 1700 ? ? ?

Spring 2300 ? 2200 ? ? ?

Summer 2400 ? 2600 ? ? ?

Total 8000 8400 9000

Average ? ? ?

=8000/4 2000

=1900/2000 0.95 =1900/2100 0.90

=8400/4 =9000/4 2250 2100

=(0.95+0.90)/2 0.925 =2250*.925 2081


1. Calculate the average leads per season for each of the past two years 2. Calculate a seasonal index for each season of the year 3. Average the indices by season 4. Calculate the average leads per season for next year by using total

forecast leads for the next year divided by the number of seasons 5. Multiply next year’s average seasonal leads by each average seasonal

index to get forecasted leads per season

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Time Series Problem

Solution

Quarter 2014 ‘14 Index 2015 ’15 Index Avg.

Index 2016

Fall 1900 0.95 1900 0.90 0.925 2081

Winter 1400 0.70 1700 0.81 0.755 1699

Spring 2300 1.15 2200 1.05 1.100 2475

Summer 2400 1.20 2600 1.24 1.220 2745

Total 8000 8400 9000

Average 2000 2100 2250


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Week

1-Bedroom Seasonality Factors

1X1

How this applies? 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy

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Measuring Forecasting Accuracy

• Forecasts are never perfect

• The forecast error is the difference between the actual value and the forecast value for

the corresponding period

Et = At - Ft

where E is the forecast error at period t, A is the actual value at period t, and F is the

forecast for period t.

• Measures of aggregate error:

- Mean Absolute Error (MAE) or Mean Absolute Deviation (MAD)

- Mean Absolute Percentage Error (MAPE) or Mean Absolute Percentage Deviation

(MAPD)

- Mean Squared Error (MSE) or Mean Squared Prediction Error (MSPE)

- Cumulative Forecast Error (CFE)


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Forecast Accuracy Problem

• An asset manager is measuring the accuracy of

her forecasts using data from the past 5

Thursdays.

• Average difference = (4+6-3-6-2)/5 = -0.2

• Is this an accurate forecast?

Forecast Actual Difference

43 39 4

40 34 6

34 37 -3

36 42 -6

38 40 -2


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Forecast Actual Difference Absolute

Difference

43 39 4 4

40 34 6 6

34 37 -3 3

36 42 -6 6

38 40 -2 2

MAE 4.2

MAE: Mean Absolute Error 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy

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Forecast Actual Difference Absolute

Difference % of Actual

43 39 4 4 10.3%

40 34 6 6 17.6%

34 37 -3 3 8.1%

36 42 -6 6 14.3%

38 40 -2 2 5.0%

MAPE 11.1%

MAPE: Mean Absolute Percent Error 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy

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Week Type Unit Category Lease Term Category

Move-in Week Etc.

Level of Granularity

Number of Days Out Measure accuracy where the forecast has the best potential for performing well

Measure accuracy with appropriate lead time so that your yielding decisions will have value

Too far out: - Decisions mean little - Typically less

accurate

Too close in: - Decisions made

too late

Key Questions when

Measuring Accuracy


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Using T-tests to Assess Unit Amenity Values

• The Problem: how do we know whether our unit

amenities are priced too high or too low (or just right)?

• The Solution: Use Days on Market (DOM) as a proxy for

market response and assess how statistically significantly

different the average DOM is for leases with versus

without the amenity

• Application: Any individual or bundle of unit-level

amenities including renovations

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Example 1

T-test examines whether 2 samples are different; commonly used with small sample sizes First two parameters are the

ranges of the two samples

Third parameter is set to 1

for one-tailed distribution

and 2 for two-tailed

Fourth parameter is set to 1

for paired data, 2 for equal

variance and 3 for unequal

variance

Conclusion: PRICED RIGHT

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Example 2

Only 3 bundles can be analyzed BA partial and Kitchen partial (26)

BA full and Kitchen full (65)

No renovations (12)

BA Minor BA Partial Kitchen Appliance Kitchen Partial BA Full Kitchen Full LseCount AvgDOM

50 75 150 175 No Amenity No Amenity 1 1.0

No Amenity 75 150 175 No Amenity No Amenity 1 33.0

No Amenity No Amenity No Amenity 1 30.0

No Amenity 175 No Amenity No Amenity 26 43.5

No Amenity 150 No Amenity No Amenity No Amenity 2 9.5

No Amenity 175 250 No Amenity 1 16.0

No Amenity No Amenity 2 44.5

No Amenity 250 450 65 78.4

No Amenity No Amenity 12 46.8

Grand Total 111 62.9

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Example 2

Conclusion: PARTIALS PRICED OK; FULL RENO PRICED TOO HIGH

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0,1,1,2,3,5,8,13,21,34,55

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Old Data

Rules

New Data

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"the map is not the territory"

“...no matter how many instances of white swans we may have observed, this does not justify the conclusion that all swans are white.”

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Pro

bab

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Sum of 3 Dice

Actual

Distribution

Mean = 10.5 Standard deviation = 2.96

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Distribution Kolmogorov Smirnov Statistic Chi-Squared Statistic Parameters

Dagum 0.03621 0.37197 k=0.34965 alpha=3.3322 beta=131.63

Burr 0.04574 0.8922 k=5.2827 a=1.4273 b=289.97

Weibull 0.13813 7.634 a=1.259 b=100.07

Perason6 0.17274 18.404 alpha1=1.553 alpha2=35.978 beta=2091.8

Average Days vacant

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Distribution Kolmogorov Smirnov Statistic Chi-Squared Statistic Parameters

Burr 0.06249 0.1269 k=146.87 alpha=15.96 beta=127.03

Weibull 0.08687 0.03283 alpha=13.597 beta=92.483

Gumbel Min 0.0707 0.08378 sigma=5.5687 mu=93.014

Pert 0.07139 0.16172 m=95.711 a=57.213 b=100.43

Occupancy

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Analyzing Performance:

Measurement Methodology

1. Methodology 2. Performance Results 3. Intangible Benefits

1. Measure “Rental Revenue” • Account for both rent and occupancy

- Method 1 – Month End Financials - Method 2 – RPU (Revenue per Unit)

2. Incorporate a Benchmark • Before and After - Pre vs. Post Revenue Management • 3rd party “market” data • Test vs. Control Data Set

3. Measure over Time • Revenue management is a marathon, not a sprint

4. Account for the Intangibles

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Method 1 - Month End Financials 1. Methodology 2. Performance Results 3. Intangible Benefits

• Measure the month end revenue line items that Rev Mgmt can directly impact: › Market Rent

› Vacancy Loss

› Loss & Gain to Lease

› Concessions – New & Renewal

› Month to Month and Short Term Lease Fees

• Don’t incorporate line items that Rev Mgmt cannot control like Bad Debt, Write Offs, etc…

July Aug Sept Oct Nov Dec Jan Feb Mar Apr May June Baseline July Aug Sept

Market Rent $883,825 $884,575 $884,575 $884,575 $884,575 $884,635 $884,635 $885,850 $885,050 $885,050 $885,075 $878,940 $878,955 $878,980 $878,965

Vacancy Loss ($100,575) ($105,145) ($113,045) ($124,755) ($129,710) ($138,758) ($145,801) ($148,955) ($152,526) ($132,854) ($116,498) ($112,907) ($101,941) ($97,407) ($94,924)

Loss to Lease ($16,966) ($15,784) ($14,793) ($13,518) ($12,378) ($11,836) ($11,221) ($11,301) ($10,686) ($10,975) ($10,126) ($10,084) ($9,965) ($10,897) ($14,484)

Gain to Lease $110 $125 $105 $230 $100 $100 $110 $135 $135 $110 $110 $5,890 $5,885 $6,413 $6,250

Concessions - Renewals ($31,629) ($34,866) ($36,552) ($14,469) ($10,343) ($13,925) ($12,010) ($3,110) ($7,820) ($17,015) ($22,490) ($19,290) ($31,230) ($24,030) ($34,430)

Concessions ($11,412) ($12,225) ($18,875) ($11,826) ($19,769) ($22,280) ($19,241) ($4,880) ($6,440) ($21,082) ($15,620) ($19,947) ($22,206) ($19,699) ($15,447)

Month to Month Fee $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Short Term Monthly Fee $775 $1,115 $64 $701 $843 $835 $706 $590 $500 $400 $675 $770 $970 $990 $1,463

Total Rev $724,128 $717,795 $701,479 $720,938 $713,318 $698,771 $697,178 $718,329 $708,213 $703,634 $721,126 $723,372 $712,357 $720,468 $734,350 $727,393

YOY -0.5% 2.3% 3.7%

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Method 2 – Revenue per Unit (RPU) 1. Methodology 2. Performance Results 3. Intangible Benefits

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Incorporate a Benchmark


86%

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96%

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100%

102%

Baseline July Aug Sept Oct

% o

f In

de

x

Test (Rev Mgmt) vs. Control (No Rev Mgmt)

Avg Net Rental Income - Test (Rev Mgmt)Avg Net Rental Income - Control (No Rev Mgmt)

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Account for the Intangibles


• Steady pricing with measured market response

• Strategic approach to pricing with more attention and visibility to amenity-based pricing

• Better, more consistent insight into competitive market space

• Movement away from market rent and toward net effective pricing

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Thank you!

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naa maximize 2015 - presentation on in-depth analytics of pricing discovery

Data & Analytics