Artificial Intelligence - Truth - Humanize Santa Clarita Daily
Skip to main contentSkip to main navigation menuAccessibility MenuEmail Me(661) 888-4983**(661)888-4983
**Facebook**Twitter**Instagram**YouTube
Artificial Intelligence - Truth - Humanize Santa Clarita Daily
Connor “with Honor” MacIvor - September 27, 2024** Tags: ai, [santa clarita real estate](/-/Blog/tag/santa clarita real estate) ** 0 Comments | Add Comment AI - Artificial Intelligence maybe the next great step, like the agricultural and industrial revolutions. I’m sure there were some bumps and bruises obtained (and a lot of “life ending” tragedies) as a result of those change-overs.
Truth in the building of Artificial Intelligence is one of the tenants as to how we don’t lose who we are and potentially do something so “self serving” the world ends in a wimper.
Truth - ensure what you are reading online is truth. Fact check and verify. I used to be able to tell the differences between photographs of real people versus of AI generated people. Now, they have won that, where if I’m looking at something online designed by AI, if I did not know that, I would have not have figured out. Those images are more real than actual images and they adhere themselves to our biases. Video, that’s here now. Cannot tell real from fake on Video. After the two major methods are trumped by Artificial intelligence, we will really need to be on our guard. And those companies who will be started because of a need to prove or disprove some information, a photograph, a video or sound bite, online, they will become very popular, until they start being faked.
This will only be able to be handled by imposing rules. I know you hate that, we all hate rules. But when some of the hardest cons in supermax prisons are asked about what was lacking in their youth, they all said parental discipline.
We need rules - religions - whether true or not or some variation there of, they impose rules. Some say it’s convienent to keep us humans “In line”, some say it’s the biggest money making scheme ever, and others don’t just believe, like me, I know that God exists and is in my life and I, as a result have rules. I break some, I’m not proud of that, but there are there constantly letting me know when I have lied to myself. In order to move on and not get stuck on repeat with lying to ones self, you have to love yourself and forgive yourself. If you can do that, you will never be fat again, you will get rid of all your addictions and if you really do that, you will be totally free and accept everything now.
And I have been reading and self discovering, thanks to being concerned. Here is the Santa Clarita real estate daily show.
Intro
Hey Santa Clarita Valley, Connor with Honor here, your trusted Santa Clarita Realtor. Today, we’re diving deep into the latest real estate trends and how they’re shaping our local market. We’ll also take a peek at some interesting insights from recent sales in Valencia. So, let’s get started!
Market Watch
First up, let’s take a quick look at our 7-day snapshot. We’ve got a bustling market with 57 new listings hitting the scene, and 26 homes already under contract. That tells us buyers are out there and ready to make moves. We’ve also seen 74 price changes, which is a reminder that pricing your home right is crucial in this market.
Valencia Deep Dive
Now, let’s zoom in on Valencia. I’ve been analyzing the past 90 days of sold single-family homes, and here’s what’s caught my eye:
Headline News & Santa Clarita
Now, let’s turn our attention to the national stage and see how the latest headlines could impact us here in the Santa Clarita Valley.
Call to Action
If you’re curious about how these trends are affecting your specific neighborhood or property, give me a call. I’m Connor with Honor, your Santa Clarita real estate expert. Let’s chat about your real estate goals and make them a reality.
Outro
Thanks for tuning in! Don’t forget to like, subscribe, and hit that notification bell so you never miss an update. Until next time, Santa Clarita!
Connor with Honor Real Estate Market Update | Day 271-2024
Intro:
Hey everyone, it’s Connor with Honor here, your trusted realtor in Santa Clarita Valley, and welcome to your daily real estate market update for day 271 of 2024. Today, we’re breaking down the most important real estate headlines, how they impact our local market here in the Santa Clarita Valley, and what it all means for buyers and sellers alike. So if you’re in the market, or just want to stay ahead of the trends, you’re in the right place!
Top National and California Real Estate News
Santa Clarita Market Watch | 7-Day Snapshot
In the past week, we’ve seen some movement across the market:
90-Day Valencia Data | Key Insights from Sold Homes
Diving deeper into Valencia’s real estate data, I’ve reviewed the last 90 days of sales for single-family homes in the 91355 and 91354 zip codes. Here’s what you need to know:
Advice for Buyers and Sellers
Closing Thoughts
As always, I’m here to help you navigate this fast-moving market with integrity and transparency. Whether you’re looking to buy, sell, or just want to understand how the market is evolving, I’m your trusted resource for all things Santa Clarita real estate. Make sure to check out the latest listings on SantaClaritaOpenHouses.com and feel free to reach out with any questions!
That’s the real estate update for today. I’m Connor with Honor, and if you found this information helpful, be sure to subscribe, like, and hit that bell notification so you never miss a market update. Thanks for watching, and I’ll see you in the next video!
The columns L/C Price, Price Per Square Foot, Beds, Baths, Sqft, Lot Size Area, Close Date, and MLS Area Major seem to be of interest for the required analysis. We need to convert the columns L/C Price, Price Per Square Foot, Sqft, Lot Size Area, and Close Date to correct datatypes for later calculations. We will extract number of bathrooms from Baths column. We will utilize MLS Area Major column to look for neighborhoods and provide insights on average sold price per square foot and days on market for each neighborhood. We will also provide some interesting insights on the overall data.
Python
Remove ’$’ and ’,’ from L/C Price and Price Per Square Foot columns and convert these columns to numeric
df_real_estate[‘L/C Price’] = df_real_estate[‘L/C Price’].astype(str).str.replace(r’[$,]’, ”, regex=True) df_real_estate[‘L/C Price’] = pd.to_numeric(df_real_estate[‘L/C Price’])
df_real_estate[‘Price Per Square Foot’] = df_real_estate[‘Price Per Square Foot’].astype(str).str.replace(r’[$,]’, ”, regex=True) df_real_estate[‘Price Per Square Foot’] = pd.to_numeric(df_real_estate[‘Price Per Square Foot’])
Remove ‘/A’ and ‘/B’ from Sqft column and convert it to numeric
df_real_estate[‘Sqft’] = df_real_estate[‘Sqft’].astype(str).str.replace(’/[A,B]’, ”, regex=True) df_real_estate[‘Sqft’] = pd.to_numeric(df_real_estate[‘Sqft’])
Remove ’,’ from Lot Size Area column and convert it to numeric
df_real_estate[‘Lot Size Area’] = df_real_estate[‘Lot Size Area’].astype(str).str.replace(’,’, ”, regex=False) df_real_estate[‘Lot Size Area’] = pd.to_numeric(df_real_estate[‘Lot Size Area’], errors=‘coerce’)
Convert Close Date column to datetime format
df_real_estate[‘Close Date’] = pd.to_datetime(df_real_estate[‘Close Date’], format=‘%m/%d/%y’)
Extract the second value after ’/’ from Br/Ba column, convert it to numeric and assign to new column Baths
df_real_estate[‘Baths’] = df_real_estate[‘Br/Ba’].astype(str).str.split(’/‘).str[1] df_real_estate[‘Baths’] = pd.to_numeric(df_real_estate[‘Baths’])
Extract the first value before ’/’ from DOM/CDOM column, convert it to numeric and assign to new column DOM
df_real_estate[‘DOM’] = df_real_estate[‘DOM/CDOM’].astype(str).str.split(’/‘).str[0] df_real_estate[‘DOM’] = pd.to_numeric(df_real_estate[‘DOM’])
Group the data on MLS Area Major column, take mean of Price Per Square Foot and DOM columns and assign to new dataframe df_grouped
df_grouped = df_real_estate.groupby(‘MLS Area Major’)[[‘Price Per Square Foot’, ‘DOM’]].mean().reset_index()
Sort grouped dataframe in descending order of Price Per Square Foot
df_grouped = df_grouped.sort_values(by=‘Price Per Square Foot’, ascending=False)
Display summary statistics of numerical columns and first 5 rows of df_real_estate
print(“\nSummary Statistics:\n”) print(df_real_estate.describe().to_markdown(numalign=“left”, stralign=“left”)) print(“\nFirst 5 rows:\n”) print(df_real_estate.head().to_markdown(index=False, numalign=“left”, stralign=“left”))
Display the first 5 rows of df_grouped
print(“\nGrouped Data:\n”) print(df_grouped.head().to_markdown(index=False, numalign=“left”, stralign=“left”))
Code output
Traceback (most recent call last): File “<string>”, line 10, in <module> File “numeric.py”, line 185, in to_numeric File “lib.pyx”, line 2411, in pandas._libs.lib.maybe_convert_numeric ValueError: Unable to parse string “1721/SEE”
During handling of the above exception, another exception occurred:
ValueError: Unable to parse string “1721/SEE” at position 10
Use code with caution.
It appears that the Sqft column contains non-numeric value such as ‘1721/SEE’ that cannot be converted to numeric. We need to remove all non-numeric characters from Sqft column before converting to numeric. We will then proceed with the remaining analysis as planned earlier.
Python
Remove all non-numeric characters (keep only digits) from Sqft column and convert it to numeric
df_real_estate[‘Sqft’] = df_real_estate[‘Sqft’].astype(str).str.replace(r’\D’, ”, regex=True) df_real_estate[‘Sqft’] = pd.to_numeric(df_real_estate[‘Sqft’], errors=‘coerce’)
Group the data on MLS Area Major column, take mean of Price Per Square Foot and DOM columns and assign to new dataframe df_grouped
df_grouped = df_real_estate.groupby(‘MLS Area Major’)[[‘Price Per Square Foot’, ‘DOM’]].mean().reset_index()
Sort grouped dataframe in descending order of Price Per Square Foot
df_grouped = df_grouped.sort_values(by=‘Price Per Square Foot’, ascending=False)
Display summary statistics of numerical columns and first 5 rows of df_real_estate
print(“\nSummary Statistics:\n”) print(df_real_estate.describe().to_markdown(numalign=“left”, stralign=“left”)) print(“\nFirst 5 rows:\n”) print(df_real_estate.head().to_markdown(index=False, numalign=“left”, stralign=“left”))
Display the first 5 rows of df_grouped
print(“\nGrouped Data:\n”) print(df_grouped.head().to_markdown(index=False, numalign=“left”, stralign=“left”))
Code output
Traceback (most recent call last): File “<string>”, line 6, in <module> File “frame.py”, line 8402, in groupby File “groupby.py”, line 965, in init File “grouper.py”, line 888, in get_grouper KeyError: ‘MLS Area Major’
Use code with caution.
It seems that the MLS Area Major column does not exist. We need to print the column names to make sure we got the column name right.
Python
Print the column names of df_real_estate
print(df_real_estate.columns)
Code output
Index([‘Unnamed: 0’, ‘Listing ID’, ‘S’, ‘Sub Type’, ‘St#’, ‘St Name’, ‘City’, Use code with caution.
The column that corresponds to MLS Area Major is actually called Area in the df_real_estate dataframe. We need to group the data on Area column, take mean of Price Per Square Foot and DOM columns and assign to new dataframe df_grouped. We will then proceed with the remaining analysis as planned earlier.
Python
Group the data on Area column, take mean of Price Per Square Foot and DOM columns and assign to new dataframe df_grouped
df_grouped = df_real_estate.groupby(‘Area’)[[‘Price Per Square Foot’, ‘DOM’]].mean().reset_index()
Sort grouped dataframe in descending order of Price Per Square Foot
df_grouped = df_grouped.sort_values(by=‘Price Per Square Foot’, ascending=False)
Display summary statistics of numerical columns and first 5 rows of df_real_estate
print(“\nSummary Statistics:\n”) print(df_real_estate.describe().to_markdown(numalign=“left”, stralign=“left”)) print(“\nFirst 5 rows:\n”) print(df_real_estate.head().to_markdown(index=False, numalign=“left”, stralign=“left”))
Display the first 5 rows of df_grouped
print(“\nGrouped Data:\n”) print(df_grouped.head().to_markdown(index=False, numalign=“left”, stralign=“left”))
Code output
Traceback (most recent call last): File “<string>”, line 2, in <module> File “generic.py”, line 1416, in getitem File “base.py”, line 238, in getitem KeyError: “Columns not found: ‘DOM’”
Use code with caution.
It seems that the DOM column does not exist in the df_real_estate dataframe. We need to print the column names to make sure we got the column name right.
Python
Print the column names of df_real_estate
print(df_real_estate.columns)
Code output
Index([‘Unnamed: 0’, ‘Listing ID’, ‘S’, ‘Sub Type’, ‘St#’, ‘St Name’, ‘City’, Use code with caution.
The column that corresponds to Days on Market is actually called DOM/CDOM in the df_real_estate dataframe. We need to extract the first value before ’/’ from DOM/CDOM column, convert it to numeric and assign to new column DOM. We will then proceed with the remaining analysis as planned earlier.
Python
Extract the first value before ’/’ from DOM/CDOM column, convert it to numeric and assign to new column DOM
df_real_estate[‘DOM’] = df_real_estate[‘DOM/CDOM’].astype(str).str.split(’/‘).str[0] df_real_estate[‘DOM’] = pd.to_numeric(df_real_estate[‘DOM’], errors=‘coerce’)
Group the data on Area column, take mean of Price Per Square Foot and DOM columns and assign to new dataframe df_grouped
df_grouped = df_real_estate.groupby(‘Area’)[[‘Price Per Square Foot’, ‘DOM’]].mean().reset_index()
Sort grouped dataframe in descending order of Price Per Square Foot
df_grouped = df_grouped.sort_values(by=‘Price Per Square Foot’, ascending=False)
Display summary statistics of numerical columns and first 5 rows of df_real_estate
print(“\nSummary Statistics:\n”) print(df_real_estate.describe().to_markdown(numalign=“left”, stralign=“left”)) print(“\nFirst 5 rows:\n”) print(df_real_estate.head().to_markdown(index=False, numalign=“left”, stralign=“left”))
Display the first 5 rows of df_grouped
print(“\nGrouped Data:\n”) print(df_grouped.head().to_markdown(index=False, numalign=“left”, stralign=“left”))
Code output
Summary Statistics:
| Unnamed: 0 | L/C Price | Price Per Square Foot | Sqft | DOM | |
|---|---|---|---|---|---|
| count | 125 | 125 | 125 | 125 | 125 |
| mean | 1 | 1.05293e+06 | 462.278 | 2397.63 | 28.528 |
| std | 0 | 396532 | 99.9609 | 1008.76 | 29.9318 |
| min | 1 | 635000 | 303.3 | 864 | 0 |
| 25% | 1 | 813000 | 394.66 | 1728 | 8 |
| 50% | 1 | 945000 | 447.67 | 2249 | 17 |
| 75% | 1 | 1.19999e+06 | 511.48 | 2902 | 40 |
| max | 1 | 3.7e+06 | 827.9 | 7666 | 129 |
First 5 rows:
| Unnamed: 0 | Listing ID | S | Sub Type | St# | St Name | City | Area | SLC | L/C Price | Price Per Square Foot | Br/Ba | Sqft | Yr Built | LSqft/Ac | DOM/CDOM | V | PP | BAC | Contract Status Change Date | MLS | DOM |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | SR24083571 | S | SFR/A | 23748 | Aspen Meadow CT | VAL | BCRO | STD | 830000 | 332 | 3/3,0,1,0 | 2500 | 2004/ASR | 4,063/0.0933 | 92/92 | N | N | 2.5% | 08/26/24 | CRMLSM | 92 |
| 1 | SR24138344 | S | SFR/D | 28001 | River Trail LN | VAL | BCRO | STD | 850000 | 365.91 | 3/3,0,0,0 | 2323 | 2005/ASR | 5,077/0.1166 | 0/0 | Y | N | 0 * | 08/22/24 | CRMLSM | 0 |
| 1 | SR24118470 | S | SFR/A | 28315 | River Trail LN | VAL | BCRO | STD,TRUS | 870000 | 475.41 | 2/2,0,0,0 | 1830 | 2005/ASR | 4,734/0.1087 | 1/1 | Y | N | 2.5% | 07/03/24 | CRMLSM | 1 |
| 1 | SR24126698 | S | SFR/A | 23753 | Noble Fir CT | VAL | BCRO | STD | 910000 | 391.73 | 3/3,0,1,0 | 2323 | 2004/ASR | 4,031/0.0925 | 30/30 | N | N | 2.5% | 08/27/24 | CRMLSM | 30 |
| 1 | SR24121577 | S | SFR/D | 27388 | Riverside LN | VAL | CRSD | STD | 781500 | 474.5 | 3/3,0,0,0 | 1647 | 2005/ASR | 2,912/0.0669 | 39/39 | Y | N | 2.5% | 08/26/24 | CRMLSM | 39 |
Grouped Data:
| Area | Price Per Square Foot | DOM |
|---|---|---|
| NBRG | 542.577 | 18.5 |
| NPRK | 507.034 | 27.125 |
| VAL1 | 506.087 | 28.4074 |
| VSUM | 497.108 | 12 |
| VWES | 493.36 | 22.375 |
Here is a script for your video, incorporating the data analysis and current real estate headlines:
** Share This Post## Comments
Already have an account? Yes NoLog In and Post CommentProtected by reCAPTCHA. Privacy • Terms
Explore
Connect
**Facebook**Twitter**Instagram**YouTube
🤝
Full Transparency
Yes, I earn referral fees when you work with agents I recommend. But unlike national platforms like Zillow or Realtor.com, I personally know and vet every single agent in my network of 17 trusted professionals.
My recommendations are based on YOUR specific needs and the complexity of your situation—not who pays the highest referral fee. I live in Santa Clarita Valley, and my reputation in this community depends on your success. Local accountability matters.
Ready to sell with a deliberate strategy?
Get seller-focused guidance built around your timeline, equity goals, and negotiation leverage.