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Over Priced Listings

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  • 1. Over Priced Listings PriceFinder Research 7/1/09 Kent Lardner www.pricefinder.com.au
  • 2. Do over priced real estate listings usually lead to unsold properties? To answer this question, PriceFinder Research conducted an analysis of properties yet to be sold and still listed on the market from 50 days (up to a maximum of 150 days). A total of 73 property listings were collected from the PriceFinder database from 13 suburbs in both NSW and QLD. The PriceFinder system was then used to estimate the price for each listing. This new estimated price was then used to calculate how much each listing was over or under priced. Our results confirmed that in nearly 50% of cases, the unsold properties had been listed at prices greater than 10% above the PriceFinder estimate. Introduction
  • 3. Methods Used
    • The variables measured and analysed included:
    • the list price itself (to see if higher priced properties take longer to sell)
    • the suburb and the total amount of listings for that location
    • median price for that suburb
    • the PriceFinder estimate
    • the percentage over/underpriced and the total days on the market
    • The data was then prepared using Excel and analysed using the statistical packages of GRETL and MINITAB.
  • 4. PriceFinder Estimation Method PriceFinder allows a user to compare the quality of the comparable sale to the subject property. The system has already adjusted for size differences, all the user needs to do is compare is the overall quality and street appeal of each property to the subject. If a comparable is not suitable, click ‘Remove and replace’.
  • 5. Results The listings tested had a median of 72 days unsold. The median percentage over-priced according to the PriceFinder estimate was 10%. Regression tests indicated a positive relationship between the number of days unsold and the total price error. As a general guide, for every 1% over priced a property would remain unsold for an extra 5 days. For example, a house over-priced by 10% would remain unsold for an extra 50 days based on this model. Almost half of the properties re-valued fell within the 10% error range. In practice this is generally accepted as the norm given the heterogeneous nature of housing.
  • 6. Conclusions and Further Discussion When a property is over priced or stays listed on the market for a long period, interest wanes. Potential buyers will often become suspicious and ignore the listing, or move on to another property that is more realistically priced. Whilst the total amount of listings on the market and median price has some level of impact on how quickly a property will sell, setting the right listing price is the most important factor. Another key consideration is how much a market may change over an extended period of time. A property listed for 50 days or longer may experience a number of changes in the market as a result of new listings and recent sales during this period. Similar properties that sell within the period at prices below the current list price will have a significant impact on any subsequent valuation of the listed home. Likewise, a significant jump in listings will also place downward pressure on the price. PriceFinder Research will continue it’s study on the relationship between listing price and days on the market. Further studies will include analysis of all listings (from 1 day to 200 days) and include total stock levels and ‘market absorption’ rates.
  • 7. Appendix 1. Graph 1 - Total Days Unsold 2. Graph 2 – List Price 3. Graph 3 – Estimated Price Error 4. Least Absolute Deviation – Test Results 5. Histogram for Price Error (%) 6. Data Used
  • 8. Graph 1 - Total Days Unsold The graph indicates that the median average for total days unsold from our sample is 72. Please note that we have selected properties that are still listed after 50 days, so this average does not reflect a total market average. Another key observation is the normality test. When the P-Value is <0.05 the data is not considered normally distributed, limiting some further model uses.
  • 9. Graph 2 – List Price The graph indicates that the median average for list prices from our sample is $500,000. The data is highly skewed with the highest priced property at $2.3M and lowest at $189k. Another key observation is the normality test. When the P-Value is <0.05 the data is not considered normally distributed, limiting some further model uses.
  • 10. Graph 3 – Estimated Price Error The graph indicates that the median average for our price error is 10%. Another key observation is the normality test. When the P-Value is <0.05 the data is not considered normally distributed, limiting some further model uses.
  • 11. Least Absolute Deviation – Test Results LAD estimates using the 73 observations 1-73 Dependent variable: Days VARIABLE COEFFICIENT STDERROR T STAT P-VALUE Percent 500.000 51.4366 9.721 <0.00001 *** Mean of dependent variable = 84.2466 Standard deviation of dep. var. = 25.1002 Sum of absolute residuals = 3567 Sum of squared residuals = 259293 Using multiple independent variables including median price, total listings and percentage over/under priced, all were found to be statistically significant. However the coefficient values for both total listings and median price was very low and hence removed from the final model in this instance. As the sample was skewed (only 50 to 150 days listed) the regression model has been included as a general guide only. LAD estimates using the 73 observations 1-73 Dependent variable: Days VARIABLE COEFFICIENT STDERROR T STAT P-VALUE Percent 130.102 36.8794 3.528 0.00074 *** Median 4.00400E-05 1.06563E-05 3.757 0.00035 *** Listings 0.631788 0.0829639 7.615 <0.00001 *** Mean of dependent variable = 84.2466 Standard deviation of dep. var. = 25.1002 Sum of absolute residuals = 1858.38 Sum of squared residuals = 79142.5
  • 12. Summary Statistics for Discrete Variables Percent Count CumCnt Percent CumPct -0.07 1 1 1.37 1.37 -0.03 1 2 1.37 2.74 0.00 7 9 9.59 12.33 0.02 1 10 1.37 13.70 0.03 5 15 6.85 20.55 0.04 4 19 5.48 26.03 0.05 4 23 5.48 31.51 0.06 3 26 4.11 35.62 0.07 2 28 2.74 38.36 0.08 4 32 5.48 43.84 0.09 3 35 4.11 47.95 0.10 4 39 5.48 53.42 0.11 3 42 4.11 57.53 0.12 4 46 5.48 63.01 0.13 3 49 4.11 67.12 0.14 3 52 4.11 71.23 0.15 2 54 2.74 73.97 0.16 1 55 1.37 75.34 0.17 1 56 1.37 76.71 0.19 5 61 6.85 83.56 0.20 2 63 2.74 86.30 0.22 2 65 2.74 89.04 0.23 1 66 1.37 90.41 0.25 1 67 1.37 91.78 0.28 1 68 1.37 93.15 0.30 2 70 2.74 95.89 0.31 2 72 2.74 98.63 0.39 1 73 1.37 100.00 N= 73 The chart and data table display the counts and cumulated percentage for price errors. For example, 47.95% of the sample are 9% and lower. Histogram for Price Error (%)
  • 13. Data Used The data table below has used listings from December 2008. PF indicates the PriceFinder estimate. Percent shows the difference between the List price and PriceFinder. Days is the total days listed. Median is the overall suburb median priced for Q4/2008 for houses. Listings is the total amount of listings for that suburb (from 1 to n days).
  • 14.