#Data sources

[jcmarchi]

Implementing Advanced Analytics in Real Estate: Using Machine Learning to Predict Market Shifts

New Post has been published on https://thedigitalinsider.com/implementing-advanced-analytics-in-real-estate-using-machine-learning-to-predict-market-shifts/

Implementing Advanced Analytics in Real Estate: Using Machine Learning to Predict Market Shifts

When it comes to the real estate industry, we have traditionally relied on local economic indicators, insights from personal networks, and comparisons of historical data to deliver market evaluations. Machine learning has disrupted many industries over the past few years, but the effects it has had in the real estate market fluctuation forecasting area have been nothing short of transformative. Drawing from my experience at Kalinka Group and Barnes International Moscow, I’ve witnessed how deep analytics has allowed us to explore massive datasets, uncover hidden patterns, and unlock predictive insights previously unimaginable. From 2025 onwards, machine learning will no longer be a utility but a strategic advantage in how real estate is approached.

The Basics of Predictive Analytics in Real Estate

Traditional real estate market analytics methods are being replaced by advanced algorithms capable of analyzing thousands of variables at once, such as property size, location, and comparable sales, which were the focus in the pre-machine learning era. Today’s variables, now addressed by machine learning, include everything from social media sentiment to infrastructure development plans, demographic shifts, neighborhood walkability scores, climate change impacts, and proximity to cultural hubs or transit lines.

For example, the team at Barnes International Moscow successfully predicted neighborhood appreciation rates using machine learning models, an accomplishment that was once unimaginable. These models synthesize data from a variety of sources, ranging from key local economic indicators to online consumer behavior, providing insights far beyond the scope of traditional human analysis.

Data Sources and Integration Challenges

Machine learning thrives on diverse qualitative data, requiring a strong data infrastructure to gather and integrate information from various sources. At Kalinka Group, we designed a roadmap for data acquisition, sourcing information from government property records, pricing trends, real-time listing platforms, and even social media sentiment to understand consumer preferences. We also utilized IoT sensors and smart home devices to measure real-time property performance metrics, enriching our forecasting models to capture everything from supply-demand dynamics to macroeconomic trends and demographic tracking.

Effective data integration is equally important. To ensure the highest degree of accuracy, we implemented rigorous validation checks, transforming raw data into actionable insights while avoiding the pitfalls of “garbage in, garbage out.”

Advanced Analytical Techniques

What excites me most about machine learning is its ability to uncover relationships that traditional statistical methods cannot identify. For instance, at Private Broker, machine learning models revealed how transit line openings influenced property values in nearby neighborhoods.

Other advanced techniques include:

These approaches provided highly accurate forecasts of market fluctuations, empowering clients to make informed investment decisions.

Practical Applications in Real Estate

Machine learning has transformed nearly every facet of real estate, offering tangible benefits to investors, developers, and property managers.

1. Predicting Market Trends

Machine learning models analyze historical data alongside external variables to forecast market conditions. For example, in a UAE-based project, we utilized ML to analyze economic data, migration patterns, and consumer preferences, uncovering a rising demand for high-end sustainable rental apartments. This insight allowed clients to capitalize on emerging opportunities.

2. Increasing Property Value

Traditional valuation methods relied on comparable sales and broker intuition. Machine learning incorporates hundreds of variables, enabling more accurate and nuanced valuations. At Kalinka Group, Automated Valuation Models (AVMs) provided transparency and speed, winning over clients with data-driven recommendations.

3. Optimizing Commercial Portfolios

Predictive analytics using machine learning forecasts vacancy rates, maintenance costs, and infrastructure impacts, guiding commercial property owners in decision-making. In a Thailand-focused project, machine learning models helped a multinational client rebalance their real estate portfolio, withdrawing from underperforming markets that previously appeared profitable on paper.

4. Identifying Emerging Markets

Machine learning identifies development hotspots by analyzing infrastructure projects, demographic trends, and local economic activities. These insights were pivotal in projects in Cyprus and Turkey, where we advised clients on confidently entering high-growth markets.

Ethical Considerations and Challenges

While machine learning offers immense potential, it also presents ethical and practical challenges.

Data Privacy

Real estate analytics often involves sensitive financial and personal data. At Kalinka Group, we implemented encryption and compliance measures to protect client data while enabling advanced analytics.

Algorithmic Bias

Machine learning models can exhibit bias, leading to discriminatory outcomes. To address this, Barnes International Moscow trained models on diverse datasets, ensuring fairness and transparency in predictive algorithms.

Conclusion

The future of machine learning in real estate is boundless. As quantum computing and more advanced AI models emerge, predictive accuracy will improve further. Real estate professionals are now equipped to anticipate market shifts with unparalleled precision, crafting bespoke strategies for each client and investment. Success will depend on strong data infrastructure, ethical practices, and the ability to combine technological knowledge with professional intuition, ensuring real estate professionals can navigate these transformative times with confidence.

[carolxia]

Data sources (not completed)

(1) Factory worker payroll (not yet completed code change) (2) Hand workers' salaries (haven't found the data yet) (3) Luxury brand financial reports (don't know how to grab the data)

[madhukumarc]

Where can businesses find reliable data for audience segmentation?

Sources Businesses Find Reliable Data for Audience Segmentation:

Businesses can find reliable data for audience segmentation from various sources, ensuring accuracy and relevance for targeted marketing efforts.

Some key sources include:

1. Zero-Party Data: 

Data that is intentionally and proactively shared by customers with businesses, enables them to personalize their marketing strategies more effectively. Also, refer to email marketing data.

2. First-Party Data: 

Data obtained directly from customer interactions on websites and sales channels. Provides authentic insights into customer behavior and preferences.

Refer also to Google Analytics, Google Search Console, and other marketing analytics platforms.

3. Surveys and Interviews: 

Gather accurate, basic information about audience interests and behaviors. Offers insights directly from the audience, helping to understand their needs and motivations.

4. Behavioral Data: 

This presents the behavior of the audience, including their actions on websites. Provides valuable insights into audience engagement and preferences. Heat maps can come to the rescue.

5. Social Media Platforms: 

This delivers information about audience interactions, such as likes, shares, saves, and comments.

Offers insights into the social engagement patterns of the audience and the type of content that is most engaging.

6. Salespeople Interactions: 

This provides firsthand insights into customer preferences, objections, behaviors, and needs, which can be valuable for refining audience segmentation strategies.

7. Customer Service Calls and Feedback: 

This offers direct feedback and firsthand information about customer preferences, needs, concerns, and behaviors.

This helps in understanding customer segments and tailoring marketing strategies to better meet their needs.

Image Content Source - LinkedIn Ads Privacy Playbook

In summary, by leveraging these diverse sources, businesses can gather comprehensive and reliable data for audience segmentation, enabling them to tailor their marketing strategies effectively.

Here's related information that you may also find helpful – Marketing Automation Statistics [Accelerate Efficiency and Sales].

[thinkaicorp]

Seamless Connectivity: Unleashing Efficiency with Enterprise Integration Services

Elevate your business's operational prowess with our cutting-edge Enterprise Integration Services. We seamlessly unite disparate systems, applications, and processes, fostering real-time collaboration, data accuracy, and streamlined workflows. Experience heightened efficiency and agility as your enterprise embraces a new era of interconnected excellence.

[marketxcel]

Primary Research vs Secondary Research: Definitions, Differences, and Examples

Both primary and secondary research holds a significant place in the researcher’s toolkit. Primary research facilitates the collection of fresh, original data, while secondary research leverages existing information to provide context and insights.

[scarycranegame]

EVERYONE SHUT THE FUCK UP AND READ THIS.

THE CURRENT VOTE COUNT IS NOT THE FINAL RESULT.

IT WILL TAKE SEVERAL DAYS TO COUNT ALL OF THE VOTES AND DETERMINE WHO WINS THE ELECTION (POSSIBLY MORE IF A RECOUNT IS NECESSARY).

IT IS VERY LIKELY THAT THE FINAL RESULTS WILL NOT LOOK LIKE THE INITIAL RESULTS.

STOP DOOMPOSTING AND LEARN HOW THIS SHIT WORKS.

[opensuse-official]

I think it is very cool how tech companies, schools, employers, and universities make it actively difficult to distance yourself from Google, Microsoft, and Apple.

Yes most Linux distros are very stable, way more secure, privacy friendly, and way more customizable. But every institution is built to make technological independence as difficult as possible.

Yelling on the internet that everyone should switch to Linux and FOSS really ignores how much of the technological world is designed to not let that happen.

[gettheorion]

[musashi]

i am begging the other leftists on my fucking dash to stop reblogging anti-voting stuff until the election is over. there are so many doomers on this website who do not have the critical thinking skills to fucking use their brains and tons of them will be genuinely swayed by what they see online.

for the love of god, queue it for after november 5th. queue all your criticism for then. unleash the fucking beast after the election is over. but it is so fucking irresponsible to be reblogging that shit now.

[xxxball-fondlerxxx]

this is the lgbtq community. the entire community. its all of them in one picture

[frogeyedape]

I am so unbelievably pissed off. FUCK HOAs

Oh, my trash/recycling bin can't be visible except on pickup day? Ok whatever fine I hate you but I can deal with this

Weekly inspections?????? FU FU FU FU FU

SECOND NOTICE ALSO WE'RE CHARGING YOU MONEY TO SEND YOU CERTIFIED MAIL OF THIS TOTALLY LEGIT TOTALLY SECOND NOTICE OF WHAT IS ACTUALLY A VIOLATION cue me: checks notes. Hmm. My recycling bin was. on the curb. on recycling pickup day. You know. The day it has to be out. The day it is motherfucking ALLOWED TO BE FUCKING OUT AND VISIBLE.

so. 1) not a violation

I have sent them the trash AND recycling pickup schedules, which are DIFFERENT, btw

I have disputed the fact of the violation

I have disputed the linking of this "violation" to a previous violation MONTHS AGO--their "first notice" in this case was a "Courtesy Notice" LITERALLY 5 MONTHS AGO and they've done so many inspections since then and my bin CLEARLY WASN'T OUT IN THOSE INTERVENING MONTHS so WTMFH

So I am posting like a crazy person here instead of sending the absolutely deranged email I almost sent (I did send a slightly less deranged version with the disputes, and requesting a hearing)

OMG. It has been. Less than one hour since I learned this fun fun news. My bin was out YESTERDAY, y'all. YESTERDAY. I am going to blow a gasket

[jcmarchi]

Keeping it PX: How AI is Bringing Patient Experiences into the 21st Century

New Post has been published on https://thedigitalinsider.com/keeping-it-px-how-ai-is-bringing-patient-experiences-into-the-21st-century/

Keeping it PX: How AI is Bringing Patient Experiences into the 21st Century

The healthcare industry is one of the fastest-evolving sectors when it comes to technological advancements and research breakthroughs. From innovative methods for administering treatments to the relentless pursuit of cures for life-threatening diseases like cancer, progress in healthcare aims to improve standards of care and healing for patients.

While these achievements are monumental, they’re not the only elements contributing to patient satisfaction. The medical care a patient receives is just one aspect of their overall journey. Equally important—but often overlooked—are the seemingly mundane interactions: scheduling appointments, handling billing, receiving test results, and following up on care.

This non-clinical side of healthcare, often lumped under the term patient experience (PX), plays a pivotal role in shaping how patients perceive their care. And thanks to advancements in artificial intelligence (AI), these interactions are catching up to the groundbreaking innovations seen in medicine today.

The High Cost of Communication Challenges 

The reality of navigating healthcare systems can be incredibly stressful for patients. A study by the National Library of Medicine revealed that “appointment scheduling can cause anxiety for patients, especially in urgent or time-sensitive situations.” This finding highlights a fundamental truth: healthcare issues are often inherently stressful and demand immediate attention or resolution.

When patients face obstacles such as prolonged wait times, difficulty reaching a representative, or an inefficient scheduling process, their stress compounds. These moments of friction can turn an already difficult situation into a nightmare.

From the provider’s perspective, convoluted scheduling processes don’t just harm PX—they can disrupt the quality of care. Physicians may fall behind or face disorganized schedules, ultimately reducing the confidence patients place in their providers. Unlike a typical bad customer experience (CX) that might result in a lost customer or negative review, poor PX can lead to much more severe consequences, including delays in critical care and worsened health outcomes.

One alarming statistic illustrates the depth of the problem: a whopping 61% of patients reportedly skip medical appointments due to scheduling issues. While self-scheduling tools are commonplace for industries like hospitality and retail, healthcare lags behind in adopting such systems. The primary challenge lies in implementing scheduling technology that meets stringent HIPAA requirements, operates seamlessly, and offers a user-friendly interface—something that is now within reach thanks to the advent of modern AI technologies.

Bringing Patient Communications into the 21st Century 

Modernizing PX requires more than breakthroughs in medicine; it demands reimagining how patients engage with healthcare systems in their daily lives. AI has already demonstrated remarkable capabilities in the medical realm, from disease detection to drug development. However, its potential extends far beyond clinical applications.

AI is poised to transform the administrative and operational aspects of PX. A patient’s journey begins well before stepping through the hospital doors and continues long after. By leveraging AI tools, healthcare providers can:

Support virtual care and self-service: AI-powered systems enable patients to manage their own care more efficiently, from scheduling appointments to accessing test results.

Seamlessly integrate data: With the help of AI, electronic health records (EHRs) and patient management systems (PMS) can be better interconnected, creating a unified experience for both patients and providers.

Streamline appointment management: Automated scheduling tools minimize human error, reduce wait times, and ensure appointments are aligned with provider availability.

Maximize ROI: Reducing no-shows through AI reminders and scheduling insights not only improves patient care but also ensures a more predictable and efficient schedule for healthcare providers.

By addressing these operational pain points, AI allows medical professionals to spend more time focusing on what they do best—delivering quality care.

AI-Driven PX: A Holistic Approach 

The healthcare industry’s AI breakthroughs in diagnostics and treatment planning have grabbed headlines, but it’s equally important to spotlight AI’s ability to transform the operational and logistical elements of patient care. This is where the patient experience (PX) often falters. A truly holistic approach to PX recognizes that patients’ interactions with their healthcare providers extend beyond clinical visits—and that these touchpoints are ripe for AI-driven enhancement.

Patients today expect more control over their healthcare journeys. They want intuitive, user-friendly tools to handle routine tasks like booking appointments, checking test results, and paying bills. AI-powered platforms can meet these expectations better than siloed point solutions by offering personalized, real-time solutions that empower patients to manage their care on their own terms.

For example, AI agents and voice -based virtual assistants provide 24/7 access to critical services. Whether it’s answering questions about medication, reporting adverse effects, facilitating appointment rescheduling, or explaining post-care instructions, these tools are designed to provide immediate and accurate support. This not only improves convenience but also ensures that patients feel valued and heard, even outside traditional office hours.

While the patient-facing benefits of AI are significant, its impact behind the scenes is equally transformative. Administrative burdens, such as scheduling conflicts or incomplete medical records, often lead to frustration for both patients and providers. AI alleviates these challenges by automating routine tasks and ensuring data accuracy. AI-powered scheduling tools analyze multiple variables—including provider availability, patient preferences, and appointment urgency—to create an optimized schedule. This reduces the likelihood of double-booking or long waits, streamlining operations for clinics and hospitals alike.

In addition, AI facilitates seamless integration of electronic health records (EHRs) and patient management systems (PMS). By harmonizing disparate data sources, AI ensures that providers have access to a complete picture of a patient’s medical history at every touchpoint. This not only improves the quality of care but also builds patient trust, as they no longer need to repeat their history or worry about errors in their records.

The Future of Patient Experience 

The role of AI in healthcare is evolving rapidly, and its influence on PX is just beginning to take shape. Its ability to optimize both patient-facing and behind-the-scenes processes positions it as a game-changer for PX. By addressing the logistical and administrative challenges that have long plagued the industry, AI is not merely improving convenience—it’s redefining the standards of care. Patients deserve an experience that matches the sophistication of the treatments they receive. With AI-powered scheduling, data integration, and virtual care solutions, healthcare organizations can provide a holistic and modernized PX that improves satisfaction, builds trust, and delivers better outcomes.

As we embrace these innovations, the industry moves closer to achieving a future where every aspect of patient care—not just the clinical—is elevated by technology. In this new era of healthcare, patients won’t just receive cutting-edge treatments; they’ll experience a seamless journey from start to finish. And that’s a breakthrough worth celebrating.

[bananonbinary]

god i miss when the internet wasn't garbage. you can't google anything these days without whatever answer you're looking for getting lost under a deluge of seo ai bullshit. cannot wait until the bubble pops and we might get useable search engines again.

[amethystsoda]

[throwing-roses-into-the-abyss]

I know this is interesting to no one because it's incredibly niche but I have a little mystery around mid-to-late 19th century mens waistcoats that I've been digging into, and so far, after digging into a bunch of local, provincial, and also national and international online archives, including but not limited to the Rijksmuseum, the V&A, LACMA, and KCI, and also asking about this on instagram and specifically asking the dutch costume society, I have found:

-9 pictures, all local to my area in eastern Netherlands, and every single one of these pictures is people wearing local folk dress -and one (1) extant garment, in the US and also made there.

that's it.

[rickybaby]

[…] my dream is to be back here with this team, racing and winning another Monaco one day

It’s only Friday my man. Can we have some time to breathe? But also go awnn