Placer Portfolio Performance Analysis

Objective: surface the shopping centers in our portfolio that generate outsized foot-traffic and loyalty so management can spotlight winners, troubleshoot laggards, and sharpen capital-allocation decisions. We leverage three Placer-derived KPIs—Visits / Sq Ft, Repeat-Visit Loyalty, Composite Quality Score—and track their evolution from 2022-2024 to reveal both structural strengths and emerging trends.

KPIs: - Visits / Sq Ft - Repeat-Visit Loyalty - Composite Quality Score

Here's a sample of the data I used for this analysis:

	Property	GLA_numeric	avg_visits_per_customer_2022	median_visits_per_customer_2022	nationwide_rank_visits_2022	nationwide_percentile_visits_2022	nationwide_outof_visits_2022	nationwide_rank_visitsPerSqft_2022	nationwide_percentile_visitsPerSqft_2022	nationwide_outof_visitsPerSqft_2022	...	median_visits_per_customer_2024	nationwide_rank_visits_2024	nationwide_percentile_visits_2024	nationwide_outof_visits_2024	nationwide_rank_visitsPerSqft_2024	nationwide_percentile_visitsPerSqft_2024	nationwide_outof_visitsPerSqft_2024	visits_2022	visits_2023	visits_2024
0	Property 1	107016	8.12	3	7801	80	40151	2545.0	87.0	21020.0	...	2	9536	76	40151	4110.0	80.0	21020.0	1661253	1528855	1490412
1	Property 2	99159	6.90	2	11771	70	40151	5454.0	74.0	21020.0	...	1	11367	71	40151	5011.0	76.0	21020.0	1194043	1291079	1286964
2	Property 3	65808	5.93	1	15983	60	40151	7613.0	63.0	21020.0	...	1	15043	62	40151	6389.0	69.0	21020.0	875297	829095	988772
3	Property 4	365897	6.55	1	1175	97	40151	4619.0	78.0	21020.0	...	1	1101	97	40151	4266.0	79.0	21020.0	4673175	5017505	5019688
4	Property 5	98775	5.05	2	13694	65	40151	7520.0	64.0	21020.0	...	2	14572	63	40151	8462.0	59.0	21020.0	1036259	1038383	1021916

5 rows × 29 columns

1) Visits/Sqft

1a) Top 10 Properties by Average Visits

• First, lets look at the top 10 properties by average visits.
• I don’t think there are too many surprises here.
• The issue with just using this metric though is that it obviously is biased towards larger properties.

Top 10 Properties by Average Visits (2022-2024):

	Property	2022 Visits	2023 Visits	2024 Visits	Average Visits
28	Property 29	4,825,087	4,991,752	5,176,547	4,997,795
3	Property 4	4,673,175	5,017,505	5,019,688	4,903,456
75	Property 76	4,517,743	4,687,365	4,966,482	4,723,863
39	Property 40	4,515,407	4,534,461	5,066,181	4,705,350
31	Property 32	4,435,693	4,380,575	4,529,579	4,448,616
51	Property 52	4,159,951	4,285,495	4,265,594	4,237,013
34	Property 35	3,798,661	4,082,306	4,429,453	4,103,473
7	Property 8	3,631,353	4,088,438	4,085,601	3,935,131
47	Property 48	3,895,502	3,850,069	4,041,524	3,929,032
68	Property 69	3,875,309	3,800,674	3,934,154	3,870,046

1b) We will now instead look at the Ranking of the our portfolio by visits/sqft per year.

The table below looks at the average, median, min, max, and standard deviation of the visits/sqft percentile by year.

• We can see that are top performers are in the 100th percentile and our bottom performers are in the 20th percentile. Having such large ranges is problematic, so we will filter out any properties that are below the 10th percentile in any year then visualize the results over time to get an understanding of trends.

Table 1: Nationwide Visits per Sqft Percentile Statistics by Year (Properties ≥ 10th Percentile)

	Year	Average Percentile	Median Percentile	Min Percentile	Max Percentile	Standard Deviation
0	2022	73.16%	78.50%	20.00%	100.00%	21.49%
1	2023	73.04%	80.50%	21.00%	100.00%	21.58%
2	2024	72.90%	78.50%	24.00%	100.00%	21.08%

1c) Now we will look at the top 20 performers by visits/sqft.

• The plot below shows the top 20 performers by visits/sqft. I know it is quite crowded, so you can toggle the legend to hide any of the properties. Additionally, if you double click on any of the properties, you can isolate it on the plot.

2) Visits

2a) Visits Ranking by Year

• With the issues mentioned above, lets look at the visits ranking by year.

• The table below shows the average, median, min, max, and standard deviation of the visits percentile by year.

• I did the same filtering process as above to remove the outliers, however, we can see that the data is still quite left-skewed.

• Nonetheless, the median percentile of our properties is quite consistent at the 75th percentile, putting us clearly as an upper-quartile performer.

Table 2: Nationwide Visits Percentile Statistics by Year (Properties ≥ 10th Percentile)

	Year	Average Percentile	Median Percentile	Min Percentile	Max Percentile	Standard Deviation
0	2022	69.08%	75.00%	11.00%	97.00%	25.47%
1	2023	69.03%	75.00%	11.00%	97.00%	25.64%
2	2024	68.89%	75.50%	11.00%	97.00%	25.92%

2b) Visits Ranking by Year - KDE Plot

To try to provide a visual representation of the skwedness of our data, I created a distrubtion plot of the visits percentile rankings by year.

• The plot shows that the data is quite left-skewed, with the highest densities found > 80th percentile, but with the median of the most recent year being 75th percentile.

• The 75th percentile is marked with a black dotted line.

2c) Top 20 Performers by Visits

• Here I plotted the top 20 performers by visits over time. I additionally, filtered out potential left-skewed outliers by filtering our any properties that had less than 100k visits in any year.

• The plot shows that some of the owners favorite properties are shown up. However, suprisingly, it seems a few properties that recieve very little allocation or attention show up in the top 10.

• Just like the other plot above, you can toggle the legend to hide or isolate any of the properties.

3) Blended Score (visits & visits/sqft)

3a) Blended Score Ranking by Year

• Here I created a blended score that takes into account both visits and visits per sqft.

• I weighted visits by 65% and visits per sqft by 35%.

• The table below shows the top 20 performers by blended score for the most recent year.

• Interestingly, [favorite property] doesn’t show up until we make the visits weighted by 95%. On the other hand, I was suprised by the number of properties, like x, y,z that show up in the top 20 yet I honestly never think about. While we also think of x as one of our top properties, it doesn’t show up until we make the visits weighted by 90%. Look, obviously visits $\neq$ sales, but the results of this percentile analysis are interesting and I can follow up with more analysis if needed.

• Our formula for this is simple:

$$\text{Blended Score} = 0.65 \times \text{Visits Percentile} + 0.35 \times \text{Visits per Sqft Percentile}$$

Portfolio-Level Blended Score Statistics

	2022 Blended Score	2023 Blended Score	2024 Blended Score
Mean	69.83	69.64	69.46
Median	76.10	74.50	74.40

Top 20 Shopping Centers by Blended Performance Score (2024)
Weighting: 65% Visits, 35% Visits per Square Foot

	Shopping Center	Blended Score	Visits Percentile	Visits/SF Percentile	Total Visits	Visits/SF
68	Property 69	96.40	95.00	99.00	3,934,154	44.51
45	Property 46	95.45	93.00	100.00	3,346,932	165.67
13	Property 14	93.80	91.00	99.00	3,046,713	18.20
39	Property 40	93.15	97.00	86.00	5,066,181	14.01
28	Property 29	91.40	97.00	81.00	5,176,547	21.94
23	Property 24	91.30	92.00	90.00	3,108,074	16.65
3	Property 4	90.70	97.00	79.00	5,019,688	12.77
58	Property 59	90.10	88.00	94.00	2,547,526	19.16
8	Property 9	90.10	88.00	94.00	2,525,075	16.70
7	Property 8	89.75	95.00	80.00	4,085,601	12.44
78	Property 79	89.70	96.00	78.00	4,266,807	9.82
64	Property 65	89.50	86.00	96.00	2,264,797	25.20
69	Property 70	89.05	88.00	91.00	2,496,984	15.79
31	Property 32	88.30	96.00	74.00	4,529,579	12.41
54	Property 55	87.70	87.00	89.00	2,401,066	15.19
42	Property 43	87.70	87.00	89.00	2,375,442	18.41
72	Property 73	85.90	81.00	95.00	1,785,255	20.72
75	Property 76	85.80	97.00	65.00	4,966,482	10.10
44	Property 45	85.20	95.00	67.00	3,992,094	10.05
51	Property 52	85.20	95.00	67.00	4,265,594	11.25

3b) Blended Score Ranking Changes

• Here I calculated the centers that had the biggest improvements and declines in blended score from 2022 to 2024.

• Here we have score where, $\uparrow$ is good, and rank (within our portfolio), where $\downarrow$ is good.

• Obviously retenanting is a large part of the results of this analysis, for example, property 79 is likely had its decline due to the bankruptcy of 99 Cents. As this was one of the co-anchors, it likely had a large impact on the center’s performance. However, this gives us a way to quantify that drop.

Top 10 Shopping Centers with Biggest Rank Improvements (2022-2024)

	Shopping Center	2022 Score	2024 Score	Score Change	2022 Rank	2024 Rank	Rank Change
78	Property 79	76.00	87.00	11.00	36	16	-20
77	Property 78	74.50	78.00	3.50	40	27	-13
13	Property 14	86.50	95.00	8.50	14	3	-11
43	Property 44	74.00	77.00	3.00	41	31	-10
44	Property 45	77.50	81.00	3.50	30	21	-9
22	Property 23	77.50	81.00	3.50	30	21	-9
14	Property 15	76.00	78.00	2.00	36	27	-9
1	Property 2	72.00	73.50	1.50	45	38	-7
40	Property 41	50.50	58.50	8.00	61	55	-6
8	Property 9	87.50	91.00	3.50	11	5	-6

Top 10 Shopping Centers with Biggest Rank Drops (2022-2024)

	Shopping Center	2022 Score	2024 Score	Score Change	2022 Rank	2024 Rank	Rank Change
33	Property 34	77.50	66.00	-11.50	30	48	18
55	Property 56	79.00	73.00	-6.00	24	41	17
56	Property 57	79.00	73.50	-5.50	24	38	14
26	Property 27	81.00	74.50	-6.50	22	35	13
9	Property 10	90.50	87.00	-3.50	6	16	10
53	Property 54	77.00	71.00	-6.00	35	44	9
0	Property 1	83.50	78.00	-5.50	19	27	8
11	Property 12	78.00	75.50	-2.50	27	34	7
42	Property 43	91.00	88.00	-3.00	4	11	7
6	Property 7	63.00	55.00	-8.00	51	58	7

3c) Stability and Percentile Change Visualization:

• Now lets look at the most stable properties. These are our properties that have had the most consistent performance (smallest absolute score change) between 2022 and 2024. We can see that of our major properties, property 11, 63, and 73 are some of the most stable properties in our portfolio.

• Below our table of stable properties, we have a histogram to show the distribution of score changes between 2022 and 2024. We can see that the median is at 0, but that we overall had slightly more properties declining in blended score over this period than properties that improved. Rememeber this is all based on Shopping Center Percentile Rankings, not actual visits or visits/sqft. So these are all relative metrics, not absolute metrics.

Top 10 Most Stable Shopping Centers (2022-2024)

	Shopping Center	2022 Score	2024 Score	Score Change	2022 Rank	2024 Rank	Rank Change
10	Property 11	78.00	78.00	0.00	27	27	0
62	Property 63	27.00	27.00	0.00	67	66	-1
72	Property 73	88.00	88.00	0.00	9	11	2
23	Property 24	91.00	91.00	0.00	4	5	1
68	Property 69	97.00	97.00	0.00	2	1	-1
47	Property 48	68.00	67.50	-0.50	48	47	-1
19	Property 20	75.00	74.50	-0.50	38	35	-3
37	Property 38	65.00	65.50	0.50	49	49	0
66	Property 67	74.00	73.50	-0.50	41	38	-3
46	Property 47	77.50	77.00	-0.50	30	31	1

Summary Statistics of Score Changes:

	Count	Mean	Std	Min	25%	50%	75%	Max	Positive %	Negative %
Score Change	71.000000	-0.350000	4.370000	-12.000000	-2.750000	0.000000	2.000000	13.000000	41.333333	46.666667

4) Loyalty Analysis:

4a) Here we will look at the centers that have the highest and lowest amounts of loyalty in 2024 (measured by Avg. and Median visits per vistor)

• We can see that 76, 58, 37, and 60 have the highest avg. visits per customer, in 2024. However, loyalty, I believe, is such a function of the tenant mix, and so we should be careful in trying to draw any conclusions from this.

Top 10 Centers by Total Visits (2024):

	Property	Total Visits	Visits per Sqft	Avg Visits/Cust	Med Visits/Cust
0	Property 76	4,966,482	11.10	10.68	3.00
1	Property 58	2,104,485	8.86	9.81	2.00
2	Property 37	1,320,461	24.53	9.13	3.00
3	Property 49	1,411,863	15.25	9.08	3.00
4	Property 33	2,129,516	13.71	8.87	2.00
5	Property 8	4,085,601	14.00	8.76	2.00
6	Property 45	3,992,094	11.46	8.51	2.00
7	Property 65	2,264,797	23.58	8.20	2.00
8	Property 60	1,064,408	15.93	7.92	1.00
9	Property 31	632,423	9.92	7.84	2.00

Bottom 10 Centers by Total Visits (2024):

	Property	Total Visits	Visits per Sqft	Avg Visits/Cust	Med Visits/Cust
0	Property 22	35,776	2.50	1.74	1.00
1	Property 75	155,598	7.82	2.12	1.00
2	Property 7	391,832	13.04	2.44	1.00
3	Property 6	531,254	3.51	2.64	1.00
4	Property 39	366,316	11.04	2.66	1.00
5	Property 71	197,450	14.91	2.68	1.00
6	Property 26	288,239	2.93	2.72	1.00
7	Property 18	537,082	6.42	2.73	1.00
8	Property 50	494,249	27.13	2.95	1.00
9	Property 17	136,545	8.98	3.02	1.00

4b) Now we will look at the top 15 properties by visits per customer over time.

4c) Now we will be a little more granular and look at the distribution of avg. visits per customer by year in our portfolio.

• The chart is a little messy because we have a large range of values for visits, but the chart represents the average and median visits per customer by year (red and green lines respectively) and the interquartile range (blue area) which represents the 25th and 75th percentiles.

• We can see that our average visits per customer has stayed relatively consistent over the years, at about 5.5 visits per customer, across our portfolio.

5) Composite Score:

Now we will try to construct a overall composite score for each property. We will use the following metrics:

1. Visits

2. Visits per sqft

3. Loyalty (measured by avg. visits per customer)

4. Visits Growth

5a) Z-score Index (Equal Weights)

• We will start our composite score with a simple z-score index.

• This is the most simple metric to create a combined score. What we do is standardize each metric to have a mean of 0 and a standard deviation of 1. Then we take the average of the z-scores.

• The scores represent the number of standard deviations away from the mean a property is performing on a specefic metric.

  • For example, property 79 has a scaled visits score of 1.49 meaning the visits at property 79 are 1.49 standard deviations above the mean of all properties.

Z-score formula:

$$z = \frac{x - \mu}{\sigma}$$

Top Centers by Z-Score Index

	VisitsScale	VisitsPerSF	Loyalty	VisitsGrowth	Z_Score_Index	Z_Score_Index_pct
Property
Property 79	1.491580	0.024693	1.096336	1.534852	1.036865	100.000000
Property 62	0.658050	1.530969	0.175414	1.710263	1.018674	98.734177
Property 14	0.921270	1.654434	-0.263121	1.622557	0.983785	97.468354
Property 8	1.316100	0.172851	1.491017	0.877058	0.964256	96.202532
Property 30	0.109675	1.432197	1.271750	0.745499	0.889780	94.936709
Property 45	1.316100	-0.370396	1.447163	1.096323	0.872297	93.670886
Property 76	1.645125	-0.518554	1.710284	0.570088	0.851736	92.405063
Property 9	0.789660	1.135880	-0.175414	1.447146	0.799318	91.139241
Property 69	1.316100	1.654434	0.482388	-0.438529	0.753598	89.873418
Property 40	1.645125	0.543247	-0.087707	0.833205	0.733467	88.607595

5b) Min-Max Normalized Geometric Mean

• This is a more complex metric to create a combined score. What we do is standardize each metric between 1 and 10. Then we take the geometric mean of the scores.

• We use the geometric mean because it rewards properties that are performing well on all metrics more than the arithmetic mean which would reward properties that are performing well on a few metrics more than the others.

• We also penalize properties that have a higher standard deviation in their scores (uneven performance) by adding a penalty to the geometric mean.

• We can see that the while the intial min-max normalized geometric mean looks almost the same as the z-score index, the penalized geometric mean is much different with property 79 moving from the 10th to 1st place and centers like property 62, 69, and 30 moving into the top 10.

Geometric Mean formula: $$GM = \sqrt[n]{x_1 \times x_2 \times \ldots \times x_n}$$

Penalized Geometric Mean formula: $$GM_{penalized} = GM \times (1 - \frac{\sigma}{10})$$

Top Centers by Min-Max Normalized Geometric Mean

	VisitsScale	VisitsPerSF	Loyalty	VisitsGrowth	Geo_Mean	Geo_Mean_pct
Property
Property 79	9.588235	5.563380	8.384615	9.538462	8.081830	100.000000
Property 62	7.352941	9.429577	5.961538	10.000000	8.018219	98.734177
Property 8	9.117647	5.943662	9.423077	7.807692	7.946266	97.468354
Property 14	8.058824	9.746479	4.807692	9.769231	7.793438	96.202532
Property 30	5.882353	9.176056	8.846154	7.461538	7.725872	94.936709
Property 45	9.117647	4.549296	9.307692	8.384615	7.542894	93.670886
Property 9	7.705882	8.415493	5.038462	9.307692	7.426095	92.405063
Property 76	10.000000	4.169014	10.000000	7.000000	7.349924	91.139241
Property 40	10.000000	6.894366	5.269231	7.692308	7.270672	89.873418
Property 55	7.352941	7.401408	6.942308	7.230769	7.229628	88.607595

Top Centers by Penalized Geometric Mean (Balanced Performance)

	VisitsScale	VisitsPerSF	Loyalty	VisitsGrowth	Penalized_Geo_Mean	Penalized_Geo_Mean_pct
Property
Property 55	7.352941	7.401408	6.942308	7.230769	7.080732	100.000000
Property 8	9.117647	5.943662	9.423077	7.807692	6.688305	98.734177
Property 30	5.882353	9.176056	8.846154	7.461538	6.565019	97.468354
Property 79	9.588235	5.563380	8.384615	9.538462	6.556550	96.202532
Property 62	7.352941	9.429577	5.961538	10.000000	6.519627	94.936709
Property 66	7.000000	6.260563	8.730769	6.769231	6.368749	93.670886
Property 9	7.705882	8.415493	5.038462	9.307692	6.059963	92.405063
Property 14	8.058824	9.746479	4.807692	9.769231	5.974702	91.139241
Property 45	9.117647	4.549296	9.307692	8.384615	5.861216	89.873418
Property 4	10.000000	5.753521	7.461538	6.307692	5.853494	88.607595

5c) PCA-based Composite Score

• This is a the most complex of the metrics we will look at.

• PCA is a statistical technique that transforms a set of variables into a smaller set of uncorrelated variables called principal components.

• Here, we use PCA to find the “optimal” weights for our composite score.

• This is the most data-driven of the composite scores and finds the weights that best explain the variance in the data.

• The big downside is that it is very difficult to understand the weights and the composite score is not as interpretable as the other methods.

Top Centers by PCA-based Composite Score

	VisitsScale	VisitsPerSF	Loyalty	VisitsGrowth	PCA_Power_pct
Property
Property 76	0.981013	0.363014	1.000000	0.670886	100.000000
Property 79	0.936709	0.513699	0.822785	0.949367	98.734177
Property 8	0.886076	0.554795	0.936709	0.759494	97.468354
Property 45	0.886076	0.404110	0.924051	0.822785	96.202532
Property 4	0.981013	0.534247	0.721519	0.594937	94.936709
Property 35	0.936709	0.123288	0.772152	0.873418	93.670886
Property 62	0.696203	0.931507	0.556962	1.000000	92.405063
Property 40	0.981013	0.657534	0.481013	0.746835	91.139241
Property 30	0.537975	0.904110	0.873418	0.721519	89.873418
Property 69	0.886076	0.965753	0.645570	0.379747	88.607595

5d) Final Scores (Using a combination of all ranking methods)

• Here we will create an average of the rankings across all methods.

• This will give us a more comprehensive view of the rankings across all methods.

• I think this is the best way to look at the rankings across all methods.

All Centers Ranked by Consistency Across All Ranking Methods

Rank	Property	Z-Score	Geometric Mean	Penalized Geo	PCA	Consistency
1	Property 79	100.00	100.00	96.20	98.73	98.73
2	Property 8	96.20	97.47	98.73	97.47	97.47
3	Property 62	98.73	98.73	94.94	92.41	96.20
4	Property 30	94.94	94.94	97.47	89.87	94.30
5	Property 45	93.67	93.67	89.87	96.20	93.35
6	Property 14	97.47	96.20	91.14	87.34	93.04
7	Property 76	92.41	91.14	77.22	100.00	90.19
8	Property 4	87.34	87.34	88.61	94.94	89.56
9	Property 40	88.61	89.87	87.34	91.14	89.24
10	Property 55	84.81	88.61	100.00	78.48	87.97
11	Property 9	91.14	92.41	92.41	75.95	87.97
12	Property 66	83.54	84.81	93.67	83.54	86.39
13	Property 69	89.87	86.08	81.01	88.61	86.39
14	Property 49	86.08	83.54	86.08	81.01	84.18
15	Property 33	77.22	79.75	82.28	82.28	80.38
16	Property 24	74.68	82.28	84.81	72.15	78.48
17	Property 32	72.15	77.22	73.42	84.81	76.90
18	Property 65	82.28	75.95	65.82	79.75	75.95
19	Property 59	75.95	78.48	83.54	64.56	75.63
20	Property 23	78.48	81.01	79.75	59.49	74.68
21	Property 58	73.42	72.15	60.76	86.08	73.10
22	Property 35	81.01	67.09	48.10	93.67	72.47
23	Property 29	70.89	73.42	70.89	70.89	71.52
24	Property 15	67.09	69.62	72.15	69.62	69.62
25	Property 2	64.56	70.89	75.95	63.29	68.67
26	Property 47	65.82	65.82	68.35	74.68	68.67
27	Property 11	63.29	68.35	78.48	60.76	67.72
28	Property 78	69.62	74.68	74.68	49.37	67.09
29	Property 46	79.75	64.56	45.57	77.22	66.77
30	Property 67	59.49	60.76	64.56	73.42	64.56
31	Property 37	58.23	62.03	62.03	65.82	62.03
32	Property 70	68.35	63.29	58.23	51.90	60.44
33	Property 19	53.16	56.96	69.62	46.84	56.65
34	Property 60	55.70	55.70	53.16	55.70	55.06
35	Property 73	54.43	58.23	63.29	43.04	54.75
36	Property 3	50.63	54.43	67.09	45.57	54.43
37	Property 38	48.10	51.90	59.49	58.23	54.43
38	Property 44	60.76	59.49	55.70	40.51	54.11
39	Property 41	62.03	46.84	35.44	68.35	53.16
40	Property 48	49.37	48.10	44.30	67.09	52.22
41	Property 21	43.04	49.37	54.43	54.43	50.32
42	Property 52	46.84	50.63	49.37	53.16	50.00
43	Property 25	51.90	53.16	56.96	36.71	49.68
44	Property 51	41.77	44.30	51.90	48.10	46.52
45	Property 1	45.57	43.04	40.51	50.63	44.94
46	Property 57	40.51	41.77	39.24	56.96	44.62
47	Property 13	44.30	45.57	46.84	34.18	42.72
48	Property 56	37.97	37.97	30.38	62.03	42.09
49	Property 16	34.18	40.51	50.63	37.97	40.82
50	Property 43	36.71	39.24	36.71	41.77	38.61
51	Property 54	32.91	35.44	37.97	44.30	37.66
52	Property 10	39.24	36.71	32.91	39.24	37.03
53	Property 12	31.65	34.18	43.04	31.65	35.13
54	Property 22	56.96	31.65	17.72	32.91	34.81
55	Property 20	35.44	32.91	34.18	25.32	31.96
56	Property 31	30.38	30.38	25.32	35.44	30.38
57	Property 27	26.58	29.11	31.65	30.38	29.43
58	Property 5	17.72	26.58	41.77	24.05	27.53
59	Property 64	24.05	27.85	29.11	29.11	27.53
60	Property 74	18.99	25.32	26.58	26.58	24.37
61	Property 34	20.25	20.25	22.78	27.85	22.78
62	Property 72	25.32	24.05	21.52	18.99	22.47
63	Property 39	27.85	22.78	16.46	21.52	22.15
64	Property 68	29.11	18.99	15.19	20.25	20.89
65	Property 61	16.46	21.52	27.85	12.66	19.62
66	Property 53	21.52	15.19	18.99	16.46	18.04
67	Property 42	11.39	13.92	20.25	22.78	17.09
68	Property 50	22.78	17.72	12.66	15.19	17.09
69	Property 18	15.19	16.46	24.05	11.39	16.77
70	Property 77	12.66	11.39	10.13	17.72	12.97
71	Property 63	10.13	12.66	11.39	10.13	11.08
72	Property 36	13.92	10.13	1.27	13.92	9.81
73	Property 28	6.33	8.86	13.92	8.86	9.49
74	Property 7	7.59	7.59	7.59	5.06	6.96
75	Property 6	5.06	5.06	8.86	7.59	6.65
76	Property 71	8.86	6.33	3.80	6.33	6.33
77	Property 75	3.80	3.80	6.33	3.80	4.43
78	Property 17	2.53	2.53	5.06	2.53	3.16
79	Property 26	1.27	1.27	2.53	1.27	1.58

Conclusion — Why the Portfolio-Level Placer Analysis Matters

Looking asset-by-asset tells only half the story.
Raw visit counts or single-year rent rolls highlight obvious stars, but they miss three critical angles:

1. Spatial productivity (Visits / Sq Ft)
   
2. Customer stickiness (Average & median repeat visits)
   
3. Trajectory (percentile rank changes year-over-year)

By stitching those metrics together—and expressing them as percentiles, z-scores, and blended indices—we surface patterns invisible in a one-to-one review:

Traditional view	Portfolio analytics view
“Big boxes pull the most traffic.”	Several mid-size assets deliver top-decile Visits / Sq Ft, turning compact GLA into outsized footfall.
“Anchor closures drive performance.”	Rank-change tables pinpoint the exact year a dip began, often months before NOI feels the hit.
“High traffic = success.”	Loyalty overlays reveal centers with tourist-style visits but weak repeat engagement, guiding merchandising fixes.

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What stakeholders gain

• Sharper capital allocation
  Composite scores rank centers by balanced performance, ensuring cap-ex flows to assets with both high volume and high efficiency.

• Evidence-based leasing & marketing
  Loyalty laggards with strong Visits / Sq Ft become priority targets for tenant mix tweaks, community events, or way-finding upgrades.

• Early risk detection
  Assets sliding down percentile ranks trigger “yellow-flag” reviews before occupancy or rent metrics deteriorate.

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Path forward

Horizon	Action	Outcome
Now	Audit any POI boundaries that produce extreme metrics and refresh the dashboard.	Removes noise; prevents skewed investment decisions.
60 days	Layer in sales or leasing KPIs (e.g., sales per visit, rent-to-traffic ratios) to the blended index.	Links foot-traffic quality to revenue, improving ROI targeting.
Quarterly	Publish an auto-updating dashboard with threshold alerts for rank drops or loyalty spikes.	Keeps asset managers focused on real-time movers, not lagging reports.
Annual	Feed composite metrics into predictive models to forecast NOI growth and prioritize hold/refi/sell decisions.	Turns historical insight into forward-looking strategy.

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Bottom line: the Placer.ai portfolio analysis converts raw visit logs into a quantitative early-warning and opportunity-spotting system—one that sees beyond headline traffic numbers and equips decision-makers to steer capital, leasing, and marketing with confidence.