Top Real Estate Agents in Alessandro Heights, Riverside, CA
November 2026 · Ranked by observed listing activity over 12 months
Data through · Updated monthly · Edition details
Alessandro Heights at a glance
41 agents & teams · 54 homesBased on Cribber’s trailing-12-month listing-activity data, Brad Alewine ranks first in Alessandro Heights, Riverside, CA with 9 credited listings.
Leading agents by observed listing activity, Nov 10, 2025 – Nov 10, 2026.
Homes listed leaders
- 1Brad Alewine9 listings
- T-2Scott Tucker2 listings
- T-2Colleen Griggs2 listings
- T-2Charlotte Mckenzie2 listings
Asking volume leaders
- 1Brad Alewine$18.4M
- 2Andrew Villalobos$5.9M
- 3Scott Tucker$5.1M
A home’s history does not end when it leaves the market. Cribber keeps qualifying past listings in the comparison, helping you see an agent’s broader listing record over time. How rankings work
Agent leaderboard
· updated monthly · Data through
41 agents & teams · 54 homes · Nov 10, 2025 – Nov 10, 2026
| Rank | Agent / team | Listing share | ||
|---|---|---|---|---|
| 1 | Brad AlewineCompass | 9 | $18.4M | 16.67% listing share |
| T-2 | Scott TuckerProvidence Realty | 2 | $5.1M | 3.70% listing share |
| T-2 | Colleen GriggsCompass | 2 | $2.9M | 3.70% listing share |
| T-2 | Charlotte MckenzieColdwell Banker Realty | 2 | $2.1M | 3.70% listing share |
| T-5 | Andrew VillalobosColdwell Banker Realty | 1 | $5.9M | 1.85% listing share |
| T-5 | Kris HansenColdwell Banker Assoc.Brks-CL | 1 | $3.8M | 1.85% listing share |
| T-5 | Lori SolizKeller Williams Realty | 1 | $3M | 1.85% listing share |
| T-5 | James BaschMaximum Exposure Real Estate | 1 | $2.9M | 1.85% listing share |
| T-5 | Vinod DesarBerkshire Hathaway HomeServices California Realty | 1 | $2.8M | 1.85% listing share |
| T-5 | Gary BottomCompass | 1 | $2.5M | 1.85% listing share |
| T-5 | Mia HwangWindermere Real Estate Tower P | 1 | $2.5M | 1.85% listing share |
| T-5 | Leniece ArevaloVictoria Murillo, Broker | 1 | $2.3M | 1.85% listing share |
| T-5 | John L Johnson IiJ-N-J Realty | 1 | $2.2M | 1.85% listing share |
| T-5 | Susan WuNationwide Realty | 1 | $2M | 1.85% listing share |
| T-5 | Tejia FengHomeSmart | 1 | $1.8M | 1.85% listing share |
| T-5 | Brett MaclennanHill Realty Group | 1 | $1.7M | 1.85% listing share |
| T-5 | Athelle SingsonNTMR Holdings Inc. | 1 | $1.6M | 1.85% listing share |
| T-5 | Marni JimenezGrove Realty | 1 | $1.6M | 1.85% listing share |
| T-5 | Mauri JordanMoreno Beach Investments | 1 | $1.5M | 1.85% listing share |
| T-5 | Barbara Mills DuboisBetter Homes And Gardens Real Estate Champions | 1 | $1.5M | 1.85% listing share |
Showing 1–20 of 41 agents
Understanding these numbers ↓Ranks are calculated across all 41 agents. Tied agents share a rank, including across pages.
Reading the rankings
- Listings
- Distinct qualifying homes credited to an agent or team during the reporting window. Current and past listings contribute under the same rules; co-listing agents each receive credit.
- Asking volume
- The combined selected observed asking prices of credited homes. A volume rank requires a usable asking price for every qualifying home.
About the data
November 2026 edition
- Reporting window
- –
- Coverage
- 41 agents & teams · 54 unique homes
- Published
- Data cutoff
Updated monthly. This edition preserves its published figures; daily ingestion does not change these rankings.
Methodology & supporting evidence
What counts as a listing?
Ranked by distinct homes credited over the trailing 12 months, including both endpoint UTC dates. Qualifying current and past sale listings contribute when their selected listing episode overlaps the reporting window. Recorded sales identify sold homes within that listing record; other off-market homes remain inactive.
How are agents credited?
A canonical, unit-aware property identity keeps repeat observations of the same home from becoming extra credits. The agents-listings-v7 reader selects the latest qualifying episode before geographic filters. Explicit distinct co-listing agents each receive full credit. Named teams can receive documented credit; team or brokerage membership alone does not create it. Ambiguous attribution is withheld.
How are areas defined?
Municipalities use official Census boundaries and coordinate assignments. Neighborhoods use retained high-confidence polygon assignments. A home may appear in its city and neighborhood comparisons, once within each geography.
How do ties and share work?
The authoritative reader orders Homes Listed by credited homes, with asking volume and stable agent identity resolving display order. Equal listing counts share a competition rank, including across pages. Observed listing share is credited homes divided by unique eligible homes in the edition; co-listing credits can overlap.
How are asking prices measured?
Asking Volume adds the latest usable observed asking price associated with each selected episode. A volume rank requires 100% usable price coverage. These are asking prices rather than recorded sale proceeds. Homes with missing prices still count toward Homes Listed. Typical Asking Price describes the stored median and priced-home sample; historical price-based standings retain their original five-home and 90% coverage eligibility policy.
When is the leaderboard updated?
Each geography freezes the first successful verified capture for its UTC monthly edition. Read the actual reporting window, cutoff and publication date on the page. Daily ingestion prepares evidence separately. Published historical figures are preserved; a correction creates a new revision.
How can I verify this comparison?
Each comparison covers the geography, reporting window and qualifying records stated in the edition. Supporting listings show the evidence behind the figures. Payment never changes eligibility, counts or rank.
Calculation: agents-listings-v7. Publication: 2238. Quality checks: passed.
Sources & reuse
Cite this leaderboard
Cribber. Top Real Estate Agents in Alessandro Heights, Riverside, CA. November 2026 edition. Listings and asking volume, Nov 10, 2025–Nov 10, 2026. Published Nov 10, 2026.
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Markdown is readable text. JSON includes ranking data, dates, definitions, and evidence links.
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