HOW IS THE HOMELESS RATE MEASURED IN THE PHILIPPINES?

HOW IS THE HOMELESS RATE MEASURED IN THE PHILIPPINES?

When one reads that there are 4.5 million homeless individuals in the Philippines, the question immediately arises: Measured how? That figure, widely circulated and quoted, is built on shifting sands. Before we can even talk about remedies or policies, we must pause and examine the foundations.

The slippery definition problem

First, what do we mean by “homeless”? In everyday parlance, it might mean “people without a house.” But in social science, in policy, and in comparative studies, the term is more nuanced. Globally, a person is often considered homeless if they lack a decent place to sleep at night, including those in temporary shelters or unfit dwellings. Under that broad definition, those living in barong-barong (makeshift shanties) or informal settlements might well be counted. That has profound implications for the size of the “homeless” population.

In the Philippines, the lack of a uniform, official definition means that estimates vary wildly. Sometimes “homeless” is restricted to those sleeping in parks, sidewalks, or other public spaces. In other surveys or reports, it is broadened to include informal settlers, squatters, or even households living in inadequate housing forms. The Philippine Statistics Authority (PSA) technical notes say that “homeless refers to individuals or households living in the streets or public … spaces.” But that narrow definition excludes many precarious households who are often grouped under “informal settlers,” “slum dwellers,” or “urban poor.” In the 2020 Census, the PSA said that those living in streets or public spaces “who have no usual place of residence … should be listed where they are found.”

Thus, even before counting, we confront definitional ambiguity. And the ambiguity is meaningful: adopting the broader definition would “balloon” the numbers, as you noted.

Complicating matters is the notion of a housing backlog—a frequently used term in Philippine housing policy. The backlog is often defined as the number of housing units needed to meet demand (for those who can afford them). It is not equivalent to homelessness; many people in the housing backlog are simply inadequately housed (crowded residences, informal structures) rather than totally homeless. Yet in policy discourse, the two sometimes become conflated.

How the state (tries to) measure homelessness

Given the definitional murk, what empirical methods does the Philippine government (and allied organizations) use?

1. Census inclusion / enumeration of institutional populations
   The PSA’s Census of Population and Housing (CPH) collects data about housing types, tenure status, and living arrangements. In principle, this can flag households without formal housing. The PSA also collects data on “institutional populations”—those in shelters, dormitories, and other non-household living quarters.
   
   In practice, the 2024 Census (Community-Based Monitoring System, or POPCEN-CBMS) explicitly included a “midnight survey” of homeless individuals in Bacolod City, counting 127 persons living on sidewalks.

That shows that enumeration of the homeless is possible if done intentionally.

2. Administrative and social welfare records
   The Department of Social Welfare and Development (DSWD) tracks clients served, including “homeless street families” under its programs. But these are service-based counts—only those who interact with or are reached by the government or NGOs. Many homeless people never register for or access formal services, so they remain “invisible” in the records.
   
   

3. Proxies and estimates from civil society / international bodies
   Non-government organizations, universities, and international agencies often make projections or estimates (e.g. 4.5 million homeless, or 250,000 children in street situations). The OHCHR, in a joint submission, cites that estimate, noting that two-thirds of them may be in Metro Manila. But those are approximations, often based on extrapolating from small studies.
   
   

4. Relocation and shelter statistics
   The PSA also reports on relocated populations, and on households living in relocation areas or in rent-free houses without the consent of owners. In the 2020 census, for example, 57,281 persons were living in relocation areas, while only 12,615 were counted as homeless (i.e. in streets or public spaces).
   
   That gap hints at a mismatch: many people live in conditions arguably akin to homelessness (relocation areas, informal shelters), but are not counted under the narrow “homeless” label.

What the numbers suggest (and hide)

• The oft-quoted 4.5 million homeless is widely repeated in media and civil society. Some sources state that about 250,000 children are “in street situations,” though that may be a severe underestimate (others suggest up to 1 million).
  
  

• One study claims that of the 4.5 million, two-thirds (≈ 3 million) live in Metro Manila. 

• Yet the PSA’s 2020 census counted only 12,615 persons explicitly as homeless (living in streets/public spaces) across the country. That is orders of magnitude lower than the 4.5 million claim.
  
  

• Furthermore, in the 2020 census, tens of thousands of households were recorded as “rent-free houses and lots without the consent of an owner” (59,826 households)
  
  

These discrepancies illustrate the tension between narrow enumeration and broad estimates. The narrow count (streets/public spaces) yields a small number; the broader, more inclusive definition leads to millions.

My reflections, questions, suggestions

1. Definition must come first
   Before we talk about “homeless rate,” we must agree: is homelessness only those with no shelter whatsoever (roofless), or does it include those in makeshift dwellings, unstable tenure, or substandard housing? Without consensus, any number is contestable.
   
   

2. Separate homelessness from housing backlog
   The housing backlog is a useful metric for supply and demand in housing, but it is not equivalent to homelessness. We must avoid conflating “people without decent housing” with “people without any housing,” lest both numbers become meaningless.
   
   

3. Local enumeration by LGUs
   Yes, as you suggest, tasking local government units (LGUs) to conduct street counts, midnight surveys, or periodic mapping of informal settlements would yield richer, more grounded data. LGU data—if standardized—can be aggregated upward.
   
   

4. Standardize methodology nationally
   The national government, through PSA, DSWD, and DHSUD/settlements agencies, must produce a standard protocol: a mix of point-in-time counts, service records, and household surveys. Such a protocol would define categories (roofless, sheltered, precariously housed) and count methods (surveys, mapping, administrative data).
   
   

5. Use technology and geospatial tools
   Innovations like satellite imagery, drone mapping, or geotagged passenger footfall can help detect informal settlement growth and slum densification. Some development researchers already apply machine learning to infer poverty from imagery. These tools could supplement ground enumeration.
   
   

6. Transparency and public access
   Whatever data is collected should be made publicly available (subject to privacy safeguards). Civil society, academia, journalists—all should be able to audit, verify, challenge, and build upon the data.
   
   

7. Policy must follow fact, not vice versa
   The risk is that homelessness policy becomes guided by the politically convenient numbers rather than hard realities. If we undercount, we under-invest; if we overcount, resources may be diluted. The definition and numbers must serve policy, not the other way around.
   
   

The phrase “4.5 million homeless Filipinos” has become a rhetorical touchstone. But behind it lies ambiguity: Who counts as homeless? What method was used? What is included and what is excluded? Until we settle those questions, any “homeless rate” is more guess than measurement.

If I were to propose a starting path: define categories (roofless, sheltered, precarious), pilot enumeration in several cities (e.g. Manila, Cebu, Davao), compare LGU data with national protocols, and gradually scale upward. Only then can we speak of a “homeless rate” with confidence—and then design policies proportionate to the true scale of the problem.

Ramon Ike V. Seneres, www.facebook.com/ike.seneres

iseneres@yahoo.com, senseneres.blogspot.com

09088877282/02-01-2026

IS IT POSSIBLE TO DECLARE ZERO EXTREME POVERTY ZONES?

IS IT POSSIBLE TO DECLARE ZERO EXTREME POVERTY ZONES?

Declaring a “Zero Extreme Poverty Zone” may sound like a dream, but is it really impossible? I do not think so. Perhaps not at the national level, given the scale of our economic realities, but at the local barangay level, it might be achievable—if we are clear about definitions, methods, and sustainability.

Let us first clarify: we are talking here about extreme poverty, not regular poverty. A barangay could be declared “zero extreme poverty” if no household falls below the most severe deprivation levels—say, families skipping meals, children not in school, or homes without potable water. This does not mean everyone is middle class. It simply means nobody is in the worst possible condition. Isn’t that already a meaningful first step?

The big question is how to measure it. Between the Poverty Threshold Basket (PTB) and the Multidimensional Poverty Index (MPI), my preference is the MPI. Why? Because the PTB, while simple, is outdated. It is still based on an “imaginary basket” of food and non-food goods that often excludes what modern life now requires, like internet access or mobile data. How can we measure poverty today without including digital access, when education, jobs, and even government services depend on it?

The MPI, on the other hand, looks at multiple deprivations: health, education, housing, water, sanitation, livelihood, and more. In the Philippines, we already have the Community-Based Monitoring System (CBMS) mandated by law (RA 11315), which collects household-level data. With MPI and CBMS combined, we can have an accurate picture of deprivation in every barangay. Add artificial intelligence (AI) to analyze these data, and we can create poverty maps that are not only statistical but also actionable.

Of course, declaring a barangay as a “zero extreme poverty zone” will not be simple. The first challenge is volatility. A household may move in and out of extreme poverty depending on the season. A farmer who eats three meals a day during harvest might skip meals during planting. So how do we account for that? We would need a monitoring system that is continuous, not one-time.

The second challenge is validation. If an LGU says it has achieved zero extreme poverty, who verifies it? The Philippine Statistics Authority? Local universities? Civil society groups? Without third-party validation, the declaration might be reduced to political rhetoric.

That said, there are already movements pointing us in the right direction. The Zero Extreme Poverty Philippines 2030 (ZEP2030) coalition, made up of more than 140 civil society organizations, is working to lift one million families out of extreme poverty by 2030. They use multidimensional poverty profiling combined with collective action. Pilot areas include Bohol, Bukidnon, Cebu, Eastern Samar, Quezon City, and Sarangani. While they have not yet declared “zero poverty zones,” they are moving toward that goal, barangay by barangay.

Some LGUs have shown how this could be done in practice. In Bohol, CBMS data was used to identify families most vulnerable to disasters and lack of water. In Quezon City, poverty mapping revealed that sanitation and housing were the largest deprivations—not income alone—leading to targeted interventions. And in Libjo, Dinagat Islands, MPI was applied down to the barangay level, enabling hyper-local solutions in food security, education, and health. These examples show that with political will and community participation, “functional zero extreme poverty” is possible at the micro level.

Perhaps, instead of using the term “zero poverty,” we could reframe it as “dignity-guaranteed zones” or “MPI-cleared barangays.” What this means is that everyone has access to food, education, healthcare, sanitation, and shelter—and even the dignity of a proper burial. No one is left in the worst deprivations. That is both realistic and measurable.

One policy suggestion is to reward LGUs that achieve verified zero extreme poverty status. This could be tied to the Internal Revenue Allotment (IRA) or given as performance-based grants. After all, if a barangay can demonstrate that all its families are free from extreme deprivation, should that not be celebrated and replicated?

The path forward is clear: use CBMS as the data backbone, MPI as the measurement tool, and AI as the analyzer. Bring in universities and NGOs for validation. Then, allow LGUs to pass a resolution declaring functional zero extreme poverty zones, provided the data holds up.

So, is it possible? Yes. Will it be easy? No. But then again, what is the alternative? To accept that in all 42,000 barangays of this country, we cannot find even one that can declare victory over extreme poverty? That would be an even bigger tragedy.

Declaring zero extreme poverty zones is not just aspirational. It is a challenge worth pursuing—because it tells our people that the goal is not endless suffering, but dignity for all.

Ramon Ike V. Seneres, www.facebook.com/ike.seneres
iseneres@yahoo.com, senseneres.blogspot.com

09088877282/01-31-2026

HOW TO MEASURE THE LOCAL POVERTY RATE

 HOW TO MEASURE THE LOCAL POVERTY RATE

What is the best way to measure poverty at the local level? Should we rely on the traditional Poverty Threshold Basket (PTB), or should we use the more comprehensive Multidimensional Poverty Index (MPI)? Or perhaps, as I often wonder, could we combine both methods to arrive at a more accurate and practical picture of how people are really living?

The PTB, as we know, is based on an “imaginary basket of goods.” It computes the minimum cost of basic food and non-food items such as housing, clothing, utilities, transport, education, and health. It sounds simple enough, but here’s the catch: many of the inclusions are outdated. For example, in some versions, landlines and postage are still there, but cell phone load and internet access are not. Can we still say that the PTB represents the essentials of modern life?

This is where the MPI provides a fresh perspective. Instead of focusing only on income, MPI looks at several dimensions—health, education, and living standards. In practical terms, this means checking if families have electricity, clean water, proper sanitation, and decent housing, or if children are in school and not dropping out because of costs. The MPI reflects what Nobel laureate Amartya Sen called “capability deprivations”—not just lack of money, but lack of real freedoms and opportunities.

Now, what about the Community-Based Monitoring System (CBMS)? This is where things get interesting. Mandated under Republic Act 11315, CBMS requires LGUs to collect household-level data on socio-economic conditions. In other words, it gives us the granular details—from the barangay down to individual households—that can make MPI truly relevant at the local level.

So, can we combine PTB, MPI, and CBMS? Absolutely. PTB gives us the monetary baseline; MPI gives us the multidimensional picture; CBMS gives us the local context. With artificial intelligence now widely available, there is no reason why LGUs cannot integrate and analyze these data sources to build poverty maps in real time. Imagine a dashboard where mayors can see which barangays lack toilets, which sitios have children skipping meals, or which puroks need housing upgrades. That would make decision-making faster and smarter.

There are already success stories. In Pasay City, regression models were used to generate district-level MPI, helping identify pockets of deprivation that income surveys missed. Bohol province combined CBMS and MPI to add local indicators such as disaster vulnerability—very practical in a typhoon-prone island. Quezon City, meanwhile, discovered that housing and sanitation, not just income, were the biggest contributors to urban poverty. And in Libjo, Dinagat Islands, CBMS data allowed MPI to be computed down to the barangay level, resulting in targeted interventions for food security, education, and housing.

If these examples show us anything, it is that poverty measurement is not just a statistical exercise. It has direct policy consequences. The better you measure, the better you target. Which raises an important question: shouldn’t we reward LGUs that can both measure and reduce poverty effectively? After all, the Internal Revenue Allotment (IRA) is now linked to performance indicators. Why not include poverty reduction as one of them?

In fact, some barangays in Pandi, Bulacan, have already shown what is possible. By piloting CBMS as early as the 1990s, they were able to track minimum basic needs and direct resources accordingly. The result? Programs that actually responded to what residents needed—be it livelihood tools, classrooms, or sanitation facilities.

Of course, one persistent problem remains: the poverty line versus the minimum wage. In many areas, minimum wage earners are still below the poverty threshold. This makes us ask—are wages too low, or is the threshold unrealistic? Either way, the poor remain caught in the middle.

My suggestion? Modernize the PTB by including digital access and energy costs. Strengthen MPI indicators by tailoring them to local realities—like aquaculture tools for fishing towns or burial services in remote barangays. And let CBMS be the backbone for all LGU poverty monitoring.

With AI and cloud-based platforms now available at affordable costs, there is no excuse for LGUs not to use them. Poverty measurement should no longer be guesswork. It should be precise, participatory, and transparent.

The bottom line is this: poverty measurement is not about numbers alone. It is about people. It is about whether children go to school, whether families eat three meals a day, whether homes are safe from typhoons, and yes, whether the dead are buried with dignity. Unless we measure these things properly, we cannot hope to solve them.

And so, when we ask how to measure the local poverty rate, perhaps the best answer is this: measure it as if real lives depend on it—because they do.

Ramon Ike V. Seneres, www.facebook.com/ike.seneres
iseneres@yahoo.com, senseneres.blogspot.com

09088877282/01-30-2026

STANDARDS FOR DRINKABLE TAP WATER

 STANDARDS FOR DRINKABLE TAP WATER

As it is supposed to be, if tap water is tested as potable, then it should also be drinkable. But is that always the case? I do not think so, and perhaps the majority of Filipinos also do not think so either. If potable equals drinkable, then why do many households still buy bottled water by the gallon?

Perhaps this is a class issue. The upper class will not drink tap water, no matter how “potable” the label says. The middle class may drink it when budgets are tight, but when extra money is available, they buy bottled water. The lower class, however, do not have the luxury of choice. If tap water flows from the pipe, they drink it—trusting the system, or at least hoping it is safe enough.

And that is where the real issue lies: trust.

On paper, we already have more than enough laws, regulations, and standards to ensure water safety. The Department of Health (DOH) enforces the Philippine National Standards for Drinking Water (PNSDW). These standards are comprehensive: no harmful bacteria such as E. coli, acceptable taste and odor, safe chemical levels (like lead and arsenic), and even checks for radiation. If water passes all these, it is deemed “fit for human consumption.”

But pardon me if I sound skeptical. Systems may look airtight on paper, but somewhere along the way, inefficiency—or worse, corruption—may crack the chain. A lab test is only as good as the sampling, and enforcement is only as strong as the political will of the local government.

So how do we rebuild trust?

My proposal is simple: make water testing data public and accessible in real time. In this digital age, it should be compulsory for both the DOH and the LGUs to publish their test results regularly on their websites, social media pages, or mobile apps. Imagine being able to check the quality of your barangay’s tap water from your cellphone, the same way you check the weather forecast.

Better yet, why not use technology itself as a watchdog? Internet of Things (IoT) sensors can be installed in water systems to monitor turbidity, chlorine levels, and possible contamination continuously. Data could then be transmitted live to DOH dashboards and LGU portals, where citizens can see for themselves if their tap water is truly safe.

Would that not solve the trust issue? At the very least, it gives people the chance to verify instead of simply believing.

Another layer of accountability could be achieved if DOH and LGUs publish their findings separately, as a kind of check-and-balance system. If the numbers don’t match, that should raise red flags for investigation.

But there is also the question of access. According to UNICEF and WHO, only about 47.9% of Filipinos have access to safe drinking water. That means more than half of our population still rely on unsafe, untreated, or inconsistently tested sources. To put it bluntly, millions of Filipinos are forced to gamble with their health daily.

So let me pose some hard questions:

• If water is tested as potable, does it also taste good enough for people to drink without hesitation?
  
  

• If bottled water has become a multibillion-peso industry in the Philippines, what does that say about our trust in tap water?
  
  

• Should we not demand more transparency from our water concessionaires, water districts, and LGUs, who often hide behind technical reports nobody reads?
  
  

In Metro Manila, concessionaires like Manila Water and Maynilad are monitored by the MWSS Regulatory Office, with the DOH and city health departments involved in sampling. On paper, this looks like solid oversight. But outside Metro Manila, many barangay-level systems lack the same rigorous monitoring. And when a contamination incident happens—say, a cholera outbreak—news breaks only when people get sick.

We cannot afford to treat water testing as an afterthought. Safe, drinkable water is not a privilege; it is a right.

So here is my suggestion for a long-term fix:

1. Mandatory transparency. All water test results, from barangay to national level, must be publicized online, in real time.
   
   

2. IoT monitoring. Use low-cost digital sensors to measure water quality continuously, not just during scheduled checks.
   
   

3. Citizen access. Create a mobile app or SMS-based service where anyone can check the potability of their water supply instantly.
   
   

4. Accountability audits. DOH and LGUs should publish results independently to verify one another’s claims.
   
   

5. Equity measures. The government must close the access gap so that safe, potable, and yes—drinkable—tap water flows into every household, not just into wealthier neighborhoods.
   
   

The bottom line is this: until trust is rebuilt, Filipinos will keep buying bottled water. But if we could combine science, transparency, and technology, perhaps someday we could confidently drink straight from the tap—without hesitation, without fear, and without doubt.

And that, for me, is the true standard for drinkable tap water.

Ramon Ike V. Seneres, www.facebook.com/ike.seneres
iseneres@yahoo.com, senseneres.blogspot.com

09088877282/01-29-2026