In-depth Analytics of Pricing Discovery

Transcription

1 In-depth Analytics of Pricing Discovery Donald Davidoff, D2 Demand Solutions Annie Laurie McCulloh, Rainmaker LRO Rich Hughes, RealPage

2 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

3 Results Results Forecast Model Options and Design 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy 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? Theoretical Probability: Experimental Probability: Coin: P(heads) Dice: P(6) = 1 head on a 2 sided coin = 1 out of 2 = 1 2 = 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 or 8.3% 12 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: Trial Coin T T T H T T T T H T T T T Die Head & 6 N N N Y N N N N N N N N N Trial Coin H H H H T H H T T H H H Die Head & 6 N N N N N N N N N N N N 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%

4 Principles of Forecasting 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy Forecasts contain risk and uncertainty - they are rarely perfect Some characteristics of the data used to forecast can improve accuracy Grouping of Data Forecast Accuracy Quantity of Data Forecast Accuracy Recent Data Forecast Accuracy Forecasts should be systematically evaluated over time for accuracy

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

6 Forecasting Methods 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy Qualitative Methods - Educated guesses based on human judgement and opinion - Subjective and nonmathematical Quantitative Methods - Based on mathematics - Consistent and objective - Only as good as the data on which they are based Executive Opinion Market Research Delphi Method Time Series Models Causal Models Associative Models

7 Time Series Model 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy 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) - Extrapolation - Linear prediction - Trend estimation - Growth curve - Autoregressive integrated moving average (ARIMA)

8 Time Series Examples 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy Uniform distribution between 1 and 2 Increasing trend Quadratic growth trend Seasonal Model

9 Time Series Problem - Seasonality 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy 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 What is the forecast for each quarter or season of next year?

10 Time Series Problem 2-period Moving Average 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 Quarter Index Index Avg. Index Fall 1900? 1900??? Winter 1400? 1700??? Spring 2300? 2200??? Summer 2400? 2600??? 2016 Total =8000/ =1900/ =1900/ =2250* =( )/2 =8400/ =9000/ Average??? 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy

11 Time Series Problem Solution 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy Quarter Index Index Avg. Index 2016 Fall Winter Spring Summer Total Average

12 Seasonality Factor How this applies? 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy Bedroom Seasonality Factors Week 1X1

13 Measuring Forecasting Accuracy 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy Forecasts are never perfect The forecast error is the difference between the actual value and the forecast value for the corresponding period E t = A t - F t 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)

14 Forecast Accuracy Problem 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy An asset manager is measuring the accuracy of her forecasts using data from the past 5 Thursdays. Forecast Actual Difference Average difference = ( )/5 = -0.2 Is this an accurate forecast?

15 MAE: Mean Absolute Error 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy Forecast Actual Difference Absolute Difference MAE 4.2

16 MAPE: Mean Absolute Percent Error 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy Forecast Actual Difference Absolute Difference % of Actual % % % % % MAPE 11.1%

17 Key Questions when Measuring Accuracy 1. Forecasting Model Options 2. Principles of Forecasting 3. Forecasting Methods 4. Time Series Models 5. Forecast Accuracy Week Type Unit Category Level of Granularity Lease Term Category Number of Days Out Move-in Week Measure accuracy where the forecast has the best potential for performing well Etc. 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

18 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

19 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

20 Example 2 BA Minor BA Partial Kitchen Appliance Kitchen Partial BA Full Kitchen Full LseCount AvgDOM No Amenity No Amenity No Amenity No Amenity No Amenity No Amenity No Amenity No Amenity No Amenity 175 No Amenity No Amenity No Amenity 150 No Amenity No Amenity No Amenity No Amenity No Amenity No Amenity No Amenity No Amenity No Amenity No Amenity Grand Total Only 3 bundles can be analyzed BA partial and Kitchen partial (26) BA full and Kitchen full (65) No renovations (12)

21 Example 2 Conclusion: PARTIALS PRICED OK; FULL RENO PRICED TOO HIGH

22 0,1,1,2,3,5,8,13,21,34,55

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26 Rules Old Data New Data

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29 "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.

30 Probability 16% 14% 12% 10% 8% 6% Actual Distribution 4% 2% 0% Sum of 3 Dice Mean = 10.5 Standard deviation = 2.96

31 Average Days vacant Distribution Kolmogorov Smirnov Statistic Chi-Squared Statistic Parameters Dagum k= alpha= beta= Burr k= a= b= Weibull a=1.259 b= Perason alpha1=1.553 alpha2= beta=2091.8

32 Occupancy Distribution Kolmogorov Smirnov Statistic Chi-Squared Statistic Parameters Burr k= alpha=15.96 beta= Weibull alpha= beta= Gumbel Min sigma= mu= Pert m= a= b=100.43

33 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 3 rd 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

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

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

36 % of Index Analyzing Performance: Incorporate a Benchmark 1. Methodology 2. Performance Results 3. Intangible Benefits Test (Rev Mgmt) vs. Control (No Rev Mgmt) 102% 100% 98% 96% 94% 92% 90% 88% 86% Baseline July Aug Sept Oct Avg Net Rental Income - Test (Rev Mgmt) Avg Net Rental Income - Control (No Rev Mgmt)

37 Analyzing Performance: Account for the Intangibles 1. Methodology 2. Performance Results 3. Intangible Benefits 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

38 Thank you!